[{"label": "USED-FOR", "tokens": "This paper presents an [[ algorithm ]] for << computing optical flow , shape , motion , lighting , and albedo >> from an image sequence of a rigidly-moving Lambertian object under distant illumination .", "h": ["algorithm"], "t": ["computing optical flow , shape , motion , lighting , and albedo"]}, {"label": "USED-FOR", "tokens": "This paper presents an << algorithm >> for computing optical flow , shape , motion , lighting , and albedo from an [[ image sequence ]] of a rigidly-moving Lambertian object under distant illumination .", "h": ["image sequence"], "t": ["algorithm"]}, {"label": "FEATURE-OF", "tokens": "This paper presents an algorithm for computing optical flow , shape , motion , lighting , and albedo from an << image sequence >> of a [[ rigidly-moving Lambertian object ]] under distant illumination .", "h": ["rigidly-moving Lambertian object"], "t": ["image sequence"]}, {"label": "FEATURE-OF", "tokens": "This paper presents an algorithm for computing optical flow , shape , motion , lighting , and albedo from an image sequence of a << rigidly-moving Lambertian object >> under [[ distant illumination ]] .", "h": ["distant illumination"], "t": ["rigidly-moving Lambertian object"]}, {"label": "CONJUNCTION", "tokens": "The problem is formulated in a manner that subsumes structure from [[ motion ]] , << multi-view stereo >> , and photo-metric stereo as special cases .", "h": ["motion"], "t": ["multi-view stereo"]}, {"label": "CONJUNCTION", "tokens": "The problem is formulated in a manner that subsumes structure from motion , [[ multi-view stereo ]] , and << photo-metric stereo >> as special cases .", "h": ["multi-view stereo"], "t": ["photo-metric stereo"]}, {"label": "USED-FOR", "tokens": "The << algorithm >> utilizes both [[ spatial and temporal intensity variation ]] as cues : the former constrains flow and the latter constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["spatial and temporal intensity variation"], "t": ["algorithm"]}, {"label": "HYPONYM-OF", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as << cues >> : the [[ former ]] constrains flow and the latter constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["former"], "t": ["cues"]}, {"label": "USED-FOR", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as cues : the [[ former ]] constrains << flow >> and the latter constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["former"], "t": ["flow"]}, {"label": "CONJUNCTION", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as cues : the [[ former ]] constrains flow and the << latter >> constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["former"], "t": ["latter"]}, {"label": "HYPONYM-OF", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as << cues >> : the former constrains flow and the [[ latter ]] constrains surface orientation ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["latter"], "t": ["cues"]}, {"label": "USED-FOR", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as cues : the former constrains flow and the [[ latter ]] constrains << surface orientation >> ; combining both cues enables dense reconstruction of both textured and texture-less surfaces .", "h": ["latter"], "t": ["surface orientation"]}, {"label": "USED-FOR", "tokens": "The algorithm utilizes both spatial and temporal intensity variation as cues : the former constrains flow and the latter constrains surface orientation ; combining both [[ cues ]] enables << dense reconstruction of both textured and texture-less surfaces >> .", "h": ["cues"], "t": ["dense reconstruction of both textured and texture-less surfaces"]}, {"label": "USED-FOR", "tokens": "The << algorithm >> works by iteratively [[ estimating affine camera parameters , illumination , shape , and albedo ]] in an alternating fashion .", "h": ["estimating affine camera parameters , illumination , shape , and albedo"], "t": ["algorithm"]}, {"label": "USED-FOR", "tokens": "An [[ entity-oriented approach ]] to << restricted-domain parsing >> is proposed .", "h": ["entity-oriented approach"], "t": ["restricted-domain parsing"]}, {"label": "USED-FOR", "tokens": "Like semantic grammar , [[ this ]] allows easy exploitation of << limited domain semantics >> .", "h": ["this"], "t": ["limited domain semantics"]}, {"label": "USED-FOR", "tokens": "In addition , [[ it ]] facilitates << fragmentary recognition >> and the use of multiple parsing strategies , and so is particularly useful for robust recognition of extra-grammatical input .", "h": ["it"], "t": ["fragmentary recognition"]}, {"label": "USED-FOR", "tokens": "In addition , [[ it ]] facilitates fragmentary recognition and the use of << multiple parsing strategies >> , and so is particularly useful for robust recognition of extra-grammatical input .", "h": ["it"], "t": ["multiple parsing strategies"]}, {"label": "USED-FOR", "tokens": "In addition , it facilitates fragmentary recognition and the use of [[ multiple parsing strategies ]] , and so is particularly useful for robust << recognition of extra-grammatical input >> .", "h": ["multiple parsing strategies"], "t": ["recognition of extra-grammatical input"]}, {"label": "USED-FOR", "tokens": "Representative samples from an entity-oriented language definition are presented , along with a [[ control structure ]] for an << entity-oriented parser >> , some parsing strategies that use the control structure , and worked examples of parses .", "h": ["control structure"], "t": ["entity-oriented parser"]}, {"label": "USED-FOR", "tokens": "Representative samples from an entity-oriented language definition are presented , along with a control structure for an entity-oriented parser , some << parsing strategies >> that use the [[ control structure ]] , and worked examples of parses .", "h": ["control structure"], "t": ["parsing strategies"]}, {"label": "PART-OF", "tokens": "A << parser >> incorporating the [[ control structure ]] and the parsing strategies is currently under implementation .", "h": ["control structure"], "t": ["parser"]}, {"label": "USED-FOR", "tokens": "This paper summarizes the formalism of Category Cooccurrence Restrictions -LRB- CCRs -RRB- and describes two [[ parsing algorithms ]] that interpret << it >> .", "h": ["parsing algorithms"], "t": ["it"]}, {"label": "FEATURE-OF", "tokens": "The use of CCRs leads to << syntactic descriptions >> formulated entirely with [[ restrictive statements ]] .", "h": ["restrictive statements"], "t": ["syntactic descriptions"]}, {"label": "USED-FOR", "tokens": "The paper shows how conventional [[ algorithms ]] for the analysis of context free languages can be adapted to the << CCR formalism >> .", "h": ["algorithms"], "t": ["CCR formalism"]}, {"label": "USED-FOR", "tokens": "The paper shows how conventional << algorithms >> for the analysis of [[ context free languages ]] can be adapted to the CCR formalism .", "h": ["context free languages"], "t": ["algorithms"]}, {"label": "FEATURE-OF", "tokens": "Special attention is given to the part of the parser that checks the fulfillment of [[ logical well-formedness conditions ]] on << trees >> .", "h": ["logical well-formedness conditions"], "t": ["trees"]}, {"label": "USED-FOR", "tokens": "We present a [[ text mining method ]] for finding << synonymous expressions >> based on the distributional hypothesis in a set of coherent corpora .", "h": ["text mining method"], "t": ["synonymous expressions"]}, {"label": "USED-FOR", "tokens": "We present a << text mining method >> for finding synonymous expressions based on the [[ distributional hypothesis ]] in a set of coherent corpora .", "h": ["distributional hypothesis"], "t": ["text mining method"]}, {"label": "EVALUATE-FOR", "tokens": "This paper proposes a new methodology to improve the [[ accuracy ]] of a << term aggregation system >> using each author 's text as a coherent corpus .", "h": ["accuracy"], "t": ["term aggregation system"]}, {"label": "EVALUATE-FOR", "tokens": "This paper proposes a new << methodology >> to improve the accuracy of a [[ term aggregation system ]] using each author 's text as a coherent corpus .", "h": ["term aggregation system"], "t": ["methodology"]}, {"label": "EVALUATE-FOR", "tokens": "Our proposed method improves the [[ accuracy ]] of our << term aggregation system >> , showing that our approach is successful .", "h": ["accuracy"], "t": ["term aggregation system"]}, {"label": "EVALUATE-FOR", "tokens": "Our proposed << method >> improves the accuracy of our [[ term aggregation system ]] , showing that our approach is successful .", "h": ["term aggregation system"], "t": ["method"]}, {"label": "USED-FOR", "tokens": "In this work , we present a [[ technique ]] for << robust estimation >> , which by explicitly incorporating the inherent uncertainty of the estimation procedure , results in a more efficient robust estimation algorithm .", "h": ["technique"], "t": ["robust estimation"]}, {"label": "USED-FOR", "tokens": "In this work , we present a [[ technique ]] for robust estimation , which by explicitly incorporating the inherent uncertainty of the estimation procedure , results in a more << efficient robust estimation algorithm >> .", "h": ["technique"], "t": ["efficient robust estimation algorithm"]}, {"label": "USED-FOR", "tokens": "In this work , we present a << technique >> for robust estimation , which by explicitly incorporating the [[ inherent uncertainty of the estimation procedure ]] , results in a more efficient robust estimation algorithm .", "h": ["inherent uncertainty of the estimation procedure"], "t": ["technique"]}, {"label": "USED-FOR", "tokens": "The combination of these two [[ strategies ]] results in a << robust estimation procedure >> that provides a significant speed-up over existing RANSAC techniques , while requiring no prior information to guide the sampling process .", "h": ["strategies"], "t": ["robust estimation procedure"]}, {"label": "COMPARE", "tokens": "The combination of these two strategies results in a << robust estimation procedure >> that provides a significant speed-up over existing [[ RANSAC techniques ]] , while requiring no prior information to guide the sampling process .", "h": ["RANSAC techniques"], "t": ["robust estimation procedure"]}, {"label": "COMPARE", "tokens": "In particular , our [[ algorithm ]] requires , on average , 3-10 times fewer samples than standard << RANSAC >> , which is in close agreement with theoretical predictions .", "h": ["algorithm"], "t": ["RANSAC"]}, {"label": "EVALUATE-FOR", "tokens": "The efficiency of the << algorithm >> is demonstrated on a selection of [[ geometric estimation problems ]] .", "h": ["geometric estimation problems"], "t": ["algorithm"]}, {"label": "HYPONYM-OF", "tokens": "An attempt has been made to use an [[ Augmented Transition Network ]] as a procedural << dialog model >> .", "h": ["Augmented Transition Network"], "t": ["dialog model"]}, {"label": "USED-FOR", "tokens": "The development of such a model appears to be important in several respects : as a << device >> to represent and to use different [[ dialog schemata ]] proposed in empirical conversation analysis ; as a device to represent and to use models of verbal interaction ; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs .", "h": ["dialog schemata"], "t": ["device"]}, {"label": "USED-FOR", "tokens": "The development of such a model appears to be important in several respects : as a device to represent and to use different [[ dialog schemata ]] proposed in empirical << conversation analysis >> ; as a device to represent and to use models of verbal interaction ; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs .", "h": ["dialog schemata"], "t": ["conversation analysis"]}, {"label": "USED-FOR", "tokens": "The development of such a model appears to be important in several respects : as a device to represent and to use different dialog schemata proposed in empirical conversation analysis ; as a << device >> to represent and to use [[ models ]] of verbal interaction ; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs .", "h": ["models"], "t": ["device"]}, {"label": "USED-FOR", "tokens": "The development of such a model appears to be important in several respects : as a device to represent and to use different dialog schemata proposed in empirical conversation analysis ; as a device to represent and to use [[ models ]] of << verbal interaction >> ; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs .", "h": ["models"], "t": ["verbal interaction"]}, {"label": "CONJUNCTION", "tokens": "The development of such a model appears to be important in several respects : as a device to represent and to use different dialog schemata proposed in empirical conversation analysis ; as a device to represent and to use models of verbal interaction ; as a device combining knowledge about [[ dialog schemata ]] and about << verbal interaction >> with knowledge about task-oriented and goal-directed dialogs .", "h": ["dialog schemata"], "t": ["verbal interaction"]}, {"label": "USED-FOR", "tokens": "A standard [[ ATN ]] should be further developed in order to account for the << verbal interactions >> of task-oriented dialogs .", "h": ["ATN"], "t": ["verbal interactions"]}, {"label": "FEATURE-OF", "tokens": "A standard ATN should be further developed in order to account for the [[ verbal interactions ]] of << task-oriented dialogs >> .", "h": ["verbal interactions"], "t": ["task-oriented dialogs"]}, {"label": "USED-FOR", "tokens": "We present a practically [[ unsupervised learning method ]] to produce << single-snippet answers >> to definition questions in question answering systems that supplement Web search engines .", "h": ["unsupervised learning method"], "t": ["single-snippet answers"]}, {"label": "USED-FOR", "tokens": "We present a practically unsupervised learning method to produce single-snippet answers to definition questions in [[ question answering systems ]] that supplement << Web search engines >> .", "h": ["question answering systems"], "t": ["Web search engines"]}, {"label": "USED-FOR", "tokens": "The [[ method ]] exploits << on-line encyclopedias and dictionaries >> to generate automatically an arbitrarily large number of positive and negative definition examples , which are then used to train an svm to separate the two classes .", "h": ["method"], "t": ["on-line encyclopedias and dictionaries"]}, {"label": "USED-FOR", "tokens": "The method exploits [[ on-line encyclopedias and dictionaries ]] to generate automatically an arbitrarily large number of << positive and negative definition examples >> , which are then used to train an svm to separate the two classes .", "h": ["on-line encyclopedias and dictionaries"], "t": ["positive and negative definition examples"]}, {"label": "USED-FOR", "tokens": "The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of [[ positive and negative definition examples ]] , which are then used to train an << svm >> to separate the two classes .", "h": ["positive and negative definition examples"], "t": ["svm"]}, {"label": "COMPARE", "tokens": "We show experimentally that the proposed method is viable , that [[ it ]] outperforms the << alternative >> of training the system on questions and news articles from trec , and that it helps the search engine handle definition questions significantly better .", "h": ["it"], "t": ["alternative"]}, {"label": "USED-FOR", "tokens": "We show experimentally that the proposed method is viable , that it outperforms the alternative of training the << system >> on questions and [[ news articles ]] from trec , and that it helps the search engine handle definition questions significantly better .", "h": ["news articles"], "t": ["system"]}, {"label": "PART-OF", "tokens": "We show experimentally that the proposed method is viable , that it outperforms the alternative of training the system on questions and [[ news articles ]] from << trec >> , and that it helps the search engine handle definition questions significantly better .", "h": ["news articles"], "t": ["trec"]}, {"label": "USED-FOR", "tokens": "We show experimentally that the proposed method is viable , that it outperforms the alternative of training the system on questions and news articles from trec , and that [[ it ]] helps the << search engine >> handle definition questions significantly better .", "h": ["it"], "t": ["search engine"]}, {"label": "USED-FOR", "tokens": "We revisit the << classical decision-theoretic problem of weighted expert voting >> from a [[ statistical learning perspective ]] .", "h": ["statistical learning perspective"], "t": ["classical decision-theoretic problem of weighted expert voting"]}, {"label": "USED-FOR", "tokens": "In the case of known expert competence levels , we give [[ sharp error estimates ]] for the << optimal rule >> .", "h": ["sharp error estimates"], "t": ["optimal rule"]}, {"label": "USED-FOR", "tokens": "We analyze a [[ reweighted version of the Kikuchi approximation ]] for estimating the << log partition function of a product distribution >> defined over a region graph .", "h": ["reweighted version of the Kikuchi approximation"], "t": ["log partition function of a product distribution"]}, {"label": "FEATURE-OF", "tokens": "We analyze a reweighted version of the Kikuchi approximation for estimating the [[ log partition function of a product distribution ]] defined over a << region graph >> .", "h": ["log partition function of a product distribution"], "t": ["region graph"]}, {"label": "FEATURE-OF", "tokens": "We establish sufficient conditions for the [[ concavity ]] of our << reweighted objective function >> in terms of weight assignments in the Kikuchi expansion , and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce global optima of the Kikuchi approximation whenever the algorithm converges .", "h": ["concavity"], "t": ["reweighted objective function"]}, {"label": "USED-FOR", "tokens": "We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion , and show that a [[ reweighted version of the sum product algorithm ]] applied to the << Kikuchi region graph >> will produce global optima of the Kikuchi approximation whenever the algorithm converges .", "h": ["reweighted version of the sum product algorithm"], "t": ["Kikuchi region graph"]}, {"label": "FEATURE-OF", "tokens": "We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion , and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce [[ global optima ]] of the << Kikuchi approximation >> whenever the algorithm converges .", "h": ["global optima"], "t": ["Kikuchi approximation"]}, {"label": "FEATURE-OF", "tokens": "Finally , we provide an explicit characterization of the polytope of concavity in terms of the [[ cycle structure ]] of the << region graph >> .", "h": ["cycle structure"], "t": ["region graph"]}, {"label": "USED-FOR", "tokens": "We apply a [[ decision tree based approach ]] to << pronoun resolution >> in spoken dialogue .", "h": ["decision tree based approach"], "t": ["pronoun resolution"]}, {"label": "USED-FOR", "tokens": "We apply a decision tree based approach to [[ pronoun resolution ]] in << spoken dialogue >> .", "h": ["pronoun resolution"], "t": ["spoken dialogue"]}, {"label": "USED-FOR", "tokens": "Our [[ system ]] deals with << pronouns >> with NP - and non-NP-antecedents .", "h": ["system"], "t": ["pronouns"]}, {"label": "USED-FOR", "tokens": "Our system deals with << pronouns >> with [[ NP - and non-NP-antecedents ]] .", "h": ["NP - and non-NP-antecedents"], "t": ["pronouns"]}, {"label": "USED-FOR", "tokens": "We present a set of [[ features ]] designed for << pronoun resolution >> in spoken dialogue and determine the most promising features .", "h": ["features"], "t": ["pronoun resolution"]}, {"label": "USED-FOR", "tokens": "We present a set of features designed for [[ pronoun resolution ]] in << spoken dialogue >> and determine the most promising features .", "h": ["pronoun resolution"], "t": ["spoken dialogue"]}, {"label": "EVALUATE-FOR", "tokens": "We evaluate the << system >> on twenty [[ Switchboard dialogues ]] and show that it compares well to Byron 's -LRB- 2002 -RRB- manually tuned system .", "h": ["Switchboard dialogues"], "t": ["system"]}, {"label": "COMPARE", "tokens": "We evaluate the system on twenty Switchboard dialogues and show that [[ it ]] compares well to << Byron 's -LRB- 2002 -RRB- manually tuned system >> .", "h": ["it"], "t": ["Byron 's -LRB- 2002 -RRB- manually tuned system"]}, {"label": "USED-FOR", "tokens": "We present a new [[ approach ]] for building an efficient and robust << classifier >> for the two class problem , that localizes objects that may appear in the image under different orien-tations .", "h": ["approach"], "t": ["classifier"]}, {"label": "USED-FOR", "tokens": "We present a new approach for building an efficient and robust [[ classifier ]] for the two << class problem >> , that localizes objects that may appear in the image under different orien-tations .", "h": ["classifier"], "t": ["class problem"]}, {"label": "PART-OF", "tokens": "In contrast to other works that address this problem using multiple classifiers , each one specialized for a specific orientation , we propose a simple two-step << approach >> with an [[ estimation stage ]] and a classification stage .", "h": ["estimation stage"], "t": ["approach"]}, {"label": "CONJUNCTION", "tokens": "In contrast to other works that address this problem using multiple classifiers , each one specialized for a specific orientation , we propose a simple two-step approach with an [[ estimation stage ]] and a << classification stage >> .", "h": ["estimation stage"], "t": ["classification stage"]}, {"label": "PART-OF", "tokens": "In contrast to other works that address this problem using multiple classifiers , each one specialized for a specific orientation , we propose a simple two-step << approach >> with an estimation stage and a [[ classification stage ]] .", "h": ["classification stage"], "t": ["approach"]}, {"label": "USED-FOR", "tokens": "The estimator yields an initial set of potential << object poses >> that are then validated by the [[ classifier ]] .", "h": ["classifier"], "t": ["object poses"]}, {"label": "EVALUATE-FOR", "tokens": "This methodology allows reducing the [[ time complexity ]] of the << algorithm >> while classification results remain high .", "h": ["time complexity"], "t": ["algorithm"]}, {"label": "USED-FOR", "tokens": "The << classifier >> we use in both stages is based on a [[ boosted combination of Random Ferns ]] over local histograms of oriented gradients -LRB- HOGs -RRB- , which we compute during a pre-processing step .", "h": ["boosted combination of Random Ferns"], "t": ["classifier"]}, {"label": "FEATURE-OF", "tokens": "The classifier we use in both stages is based on a << boosted combination of Random Ferns >> over [[ local histograms of oriented gradients -LRB- HOGs -RRB- ]] , which we compute during a pre-processing step .", "h": ["local histograms of oriented gradients -LRB- HOGs -RRB-"], "t": ["boosted combination of Random Ferns"]}, {"label": "USED-FOR", "tokens": "The classifier we use in both stages is based on a boosted combination of Random Ferns over << local histograms of oriented gradients -LRB- HOGs -RRB- >> , which we compute during a [[ pre-processing step ]] .", "h": ["pre-processing step"], "t": ["local histograms of oriented gradients -LRB- HOGs -RRB-"]}, {"label": "USED-FOR", "tokens": "Both the use of [[ supervised learning ]] and working on the gradient space makes our << approach >> robust while being efficient at run-time .", "h": ["supervised learning"], "t": ["approach"]}, {"label": "USED-FOR", "tokens": "Both the use of supervised learning and working on the [[ gradient space ]] makes our << approach >> robust while being efficient at run-time .", "h": ["gradient space"], "t": ["approach"]}, {"label": "FEATURE-OF", "tokens": "We show these properties by thorough testing on standard databases and on a new << database >> made of [[ motorbikes under planar rotations ]] , and with challenging conditions such as cluttered backgrounds , changing illumination conditions and partial occlusions .", "h": ["motorbikes under planar rotations"], "t": ["database"]}, {"label": "FEATURE-OF", "tokens": "We show these properties by thorough testing on standard databases and on a new << database >> made of motorbikes under planar rotations , and with challenging [[ conditions ]] such as cluttered backgrounds , changing illumination conditions and partial occlusions .", "h": ["conditions"], "t": ["database"]}, {"label": "HYPONYM-OF", "tokens": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging << conditions >> such as [[ cluttered backgrounds ]] , changing illumination conditions and partial occlusions .", "h": ["cluttered backgrounds"], "t": ["conditions"]}, {"label": "CONJUNCTION", "tokens": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging conditions such as [[ cluttered backgrounds ]] , << changing illumination conditions >> and partial occlusions .", "h": ["cluttered backgrounds"], "t": ["changing illumination conditions"]}, {"label": "HYPONYM-OF", "tokens": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging << conditions >> such as cluttered backgrounds , [[ changing illumination conditions ]] and partial occlusions .", "h": ["changing illumination conditions"], "t": ["conditions"]}, {"label": "CONJUNCTION", "tokens": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging conditions such as cluttered backgrounds , [[ changing illumination conditions ]] and << partial occlusions >> .", "h": ["changing illumination conditions"], "t": ["partial occlusions"]}, {"label": "HYPONYM-OF", "tokens": "We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations , and with challenging << conditions >> such as cluttered backgrounds , changing illumination conditions and [[ partial occlusions ]] .", "h": ["partial occlusions"], "t": ["conditions"]}, {"label": "USED-FOR", "tokens": "A very simple improved [[ duration model ]] has reduced the error rate by about 10 % in both << triphone and semiphone systems >> .", "h": ["duration model"], "t": ["triphone and semiphone systems"]}, {"label": "EVALUATE-FOR", "tokens": "A very simple improved duration model has reduced the [[ error rate ]] by about 10 % in both << triphone and semiphone systems >> .", "h": ["error rate"], "t": ["triphone and semiphone systems"]}, {"label": "USED-FOR", "tokens": "A new << training strategy >> has been tested which , by itself , did not provide useful improvements but suggests that improvements can be obtained by a related [[ rapid adaptation technique ]] .", "h": ["rapid adaptation technique"], "t": ["training strategy"]}, {"label": "USED-FOR", "tokens": "Finally , the << recognizer >> has been modified to use [[ bigram back-off language models ]] .", "h": ["bigram back-off language models"], "t": ["recognizer"]}, {"label": "USED-FOR", "tokens": "The [[ system ]] was then transferred from the << RM task >> to the ATIS CSR task and a limited number of development tests performed .", "h": ["system"], "t": ["RM task"]}, {"label": "USED-FOR", "tokens": "The [[ system ]] was then transferred from the RM task to the << ATIS CSR task >> and a limited number of development tests performed .", "h": ["system"], "t": ["ATIS CSR task"]}, {"label": "CONJUNCTION", "tokens": "The system was then transferred from the [[ RM task ]] to the << ATIS CSR task >> and a limited number of development tests performed .", "h": ["RM task"], "t": ["ATIS CSR task"]}, {"label": "USED-FOR", "tokens": "A new [[ approach ]] for << Interactive Machine Translation >> where the author interacts during the creation or the modification of the document is proposed .", "h": ["approach"], "t": ["Interactive Machine Translation"]}, {"label": "USED-FOR", "tokens": "This paper presents a new << interactive disambiguation scheme >> based on the [[ paraphrasing ]] of a parser 's multiple output .", "h": ["paraphrasing"], "t": ["interactive disambiguation scheme"]}, {"label": "USED-FOR", "tokens": "We describe a novel [[ approach ]] to << statistical machine translation >> that combines syntactic information in the source language with recent advances in phrasal translation .", "h": ["approach"], "t": ["statistical machine translation"]}, {"label": "PART-OF", "tokens": "We describe a novel << approach >> to statistical machine translation that combines [[ syntactic information ]] in the source language with recent advances in phrasal translation .", "h": ["syntactic information"], "t": ["approach"]}, {"label": "CONJUNCTION", "tokens": "We describe a novel approach to statistical machine translation that combines [[ syntactic information ]] in the source language with recent advances in << phrasal translation >> .", "h": ["syntactic information"], "t": ["phrasal translation"]}, {"label": "PART-OF", "tokens": "We describe a novel << approach >> to statistical machine translation that combines syntactic information in the source language with recent advances in [[ phrasal translation ]] .", "h": ["phrasal translation"], "t": ["approach"]}, {"label": "USED-FOR", "tokens": "This << method >> requires a [[ source-language dependency parser ]] , target language word segmentation and an unsupervised word alignment component .", "h": ["source-language dependency parser"], "t": ["method"]}, {"label": "CONJUNCTION", "tokens": "This method requires a [[ source-language dependency parser ]] , << target language word segmentation >> and an unsupervised word alignment component .", "h": ["source-language dependency parser"], "t": ["target language word segmentation"]}, {"label": "USED-FOR", "tokens": "This << method >> requires a source-language dependency parser , [[ target language word segmentation ]] and an unsupervised word alignment component .", "h": ["target language word segmentation"], "t": ["method"]}, {"label": "CONJUNCTION", "tokens": "This method requires a source-language dependency parser , [[ target language word segmentation ]] and an << unsupervised word alignment component >> .", "h": ["target language word segmentation"], "t": ["unsupervised word alignment component"]}, {"label": "USED-FOR", "tokens": "This << method >> requires a source-language dependency parser , target language word segmentation and an [[ unsupervised word alignment component ]] .", "h": ["unsupervised word alignment component"], "t": ["method"]}, {"label": "CONJUNCTION", "tokens": "We describe an efficient decoder and show that using these [[ tree-based models ]] in combination with conventional << SMT models >> provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser .", "h": ["tree-based models"], "t": ["SMT models"]}, {"label": "USED-FOR", "tokens": "We describe an efficient decoder and show that using these [[ tree-based models ]] in combination with conventional SMT models provides a promising << approach >> that incorporates the power of phrasal SMT with the linguistic generality available in a parser .", "h": ["tree-based models"], "t": ["approach"]}, {"label": "USED-FOR", "tokens": "We describe an efficient decoder and show that using these tree-based models in combination with conventional [[ SMT models ]] provides a promising << approach >> that incorporates the power of phrasal SMT with the linguistic generality available in a parser .", "h": ["SMT models"], "t": ["approach"]}, {"label": "CONJUNCTION", "tokens": "We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of [[ phrasal SMT ]] with the << linguistic generality >> available in a parser .", "h": ["phrasal SMT"], "t": ["linguistic generality"]}, {"label": "USED-FOR", "tokens": "We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of [[ phrasal SMT ]] with the linguistic generality available in a << parser >> .", "h": ["phrasal SMT"], "t": ["parser"]}, {"label": "FEATURE-OF", "tokens": "We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the [[ linguistic generality ]] available in a << parser >> .", "h": ["linguistic generality"], "t": ["parser"]}, {"label": "FEATURE-OF", "tokens": "<< Video >> provides not only rich [[ visual cues ]] such as motion and appearance , but also much less explored long-range temporal interactions among objects .", "h": ["visual cues"], "t": ["Video"]}, {"label": "HYPONYM-OF", "tokens": "Video provides not only rich << visual cues >> such as [[ motion ]] and appearance , but also much less explored long-range temporal interactions among objects .", "h": ["motion"], "t": ["visual cues"]}, {"label": "CONJUNCTION", "tokens": "Video provides not only rich visual cues such as [[ motion ]] and << appearance >> , but also much less explored long-range temporal interactions among objects .", "h": ["motion"], "t": ["appearance"]}, {"label": "HYPONYM-OF", "tokens": "Video provides not only rich << visual cues >> such as motion and [[ appearance ]] , but also much less explored long-range temporal interactions among objects .", "h": ["appearance"], "t": ["visual cues"]}, {"label": "USED-FOR", "tokens": "We aim to capture such interactions and to construct a powerful [[ intermediate-level video representation ]] for subsequent << recognition >> .", "h": ["intermediate-level video representation"], "t": ["recognition"]}, {"label": "USED-FOR", "tokens": "First , we develop an efficient << spatio-temporal video segmentation algorithm >> , which naturally incorporates [[ long-range motion cues ]] from the past and future frames in the form of clusters of point tracks with coherent motion .", "h": ["long-range motion cues"], "t": ["spatio-temporal video segmentation algorithm"]}, {"label": "USED-FOR", "tokens": "First , we develop an efficient spatio-temporal video segmentation algorithm , which naturally incorporates << long-range motion cues >> from the past and future frames in the form of [[ clusters of point tracks ]] with coherent motion .", "h": ["clusters of point tracks"], "t": ["long-range motion cues"]}, {"label": "PART-OF", "tokens": "Second , we devise a new << track clustering cost function >> that includes [[ occlusion reasoning ]] , in the form of depth ordering constraints , as well as motion similarity along the tracks .", "h": ["occlusion reasoning"], "t": ["track clustering cost function"]}, {"label": "FEATURE-OF", "tokens": "Second , we devise a new track clustering cost function that includes << occlusion reasoning >> , in the form of [[ depth ordering constraints ]] , as well as motion similarity along the tracks .", "h": ["depth ordering constraints"], "t": ["occlusion reasoning"]}, {"label": "PART-OF", "tokens": "Second , we devise a new << track clustering cost function >> that includes occlusion reasoning , in the form of depth ordering constraints , as well as [[ motion similarity ]] along the tracks .", "h": ["motion similarity"], "t": ["track clustering cost function"]}, {"label": "EVALUATE-FOR", "tokens": "We evaluate the proposed << approach >> on a challenging set of [[ video sequences of office scenes ]] from feature length movies .", "h": ["video sequences of office scenes"], "t": ["approach"]}, {"label": "HYPONYM-OF", "tokens": "In this paper , we introduce [[ KAZE features ]] , a novel << multiscale 2D feature detection and description algorithm >> in nonlinear scale spaces .", "h": ["KAZE features"], "t": ["multiscale 2D feature detection and description algorithm"]}, {"label": "FEATURE-OF", "tokens": "In this paper , we introduce KAZE features , a novel << multiscale 2D feature detection and description algorithm >> in [[ nonlinear scale spaces ]] .", "h": ["nonlinear scale spaces"], "t": ["multiscale 2D feature detection and description algorithm"]}, {"label": "FEATURE-OF", "tokens": "In contrast , we detect and describe << 2D features >> in a [[ nonlinear scale space ]] by means of nonlinear diffusion filtering .", "h": ["nonlinear scale space"], "t": ["2D features"]}, {"label": "USED-FOR", "tokens": "In contrast , we detect and describe << 2D features >> in a nonlinear scale space by means of [[ nonlinear diffusion filtering ]] .", "h": ["nonlinear diffusion filtering"], "t": ["2D features"]}, {"label": "USED-FOR", "tokens": "The << nonlinear scale space >> is built using efficient [[ Additive Operator Splitting -LRB- AOS -RRB- techniques ]] and variable con-ductance diffusion .", "h": ["Additive Operator Splitting -LRB- AOS -RRB- techniques"], "t": ["nonlinear scale space"]}, {"label": "CONJUNCTION", "tokens": "The nonlinear scale space is built using efficient [[ Additive Operator Splitting -LRB- AOS -RRB- techniques ]] and << variable con-ductance diffusion >> .", "h": ["Additive Operator Splitting -LRB- AOS -RRB- techniques"], "t": ["variable con-ductance diffusion"]}, {"label": "USED-FOR", "tokens": "The << nonlinear scale space >> is built using efficient Additive Operator Splitting -LRB- AOS -RRB- techniques and [[ variable con-ductance diffusion ]] .", "h": ["variable con-ductance diffusion"], "t": ["nonlinear scale space"]}, {"label": "COMPARE", "tokens": "Even though our [[ features ]] are somewhat more expensive to compute than << SURF >> due to the construction of the nonlinear scale space , but comparable to SIFT , our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods .", "h": ["features"], "t": ["SURF"]}, {"label": "COMPARE", "tokens": "Even though our [[ features ]] are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to << SIFT >> , our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods .", "h": ["features"], "t": ["SIFT"]}, {"label": "COMPARE", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our [[ results ]] reveal a step forward in performance both in detection and description against previous << state-of-the-art methods >> .", "h": ["results"], "t": ["state-of-the-art methods"]}, {"label": "EVALUATE-FOR", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our << results >> reveal a step forward in performance both in [[ detection ]] and description against previous state-of-the-art methods .", "h": ["detection"], "t": ["results"]}, {"label": "CONJUNCTION", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our results reveal a step forward in performance both in [[ detection ]] and << description >> against previous state-of-the-art methods .", "h": ["detection"], "t": ["description"]}, {"label": "EVALUATE-FOR", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our results reveal a step forward in performance both in [[ detection ]] and description against previous << state-of-the-art methods >> .", "h": ["detection"], "t": ["state-of-the-art methods"]}, {"label": "EVALUATE-FOR", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our << results >> reveal a step forward in performance both in detection and [[ description ]] against previous state-of-the-art methods .", "h": ["description"], "t": ["results"]}, {"label": "EVALUATE-FOR", "tokens": "Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space , but comparable to SIFT , our results reveal a step forward in performance both in detection and [[ description ]] against previous << state-of-the-art methods >> .", "h": ["description"], "t": ["state-of-the-art methods"]}, {"label": "USED-FOR", "tokens": "[[ Creating summaries ]] on lengthy Semantic Web documents for quick << identification of the corresponding entity >> has been of great contemporary interest .", "h": ["Creating summaries"], "t": ["identification of the corresponding entity"]}, {"label": "USED-FOR", "tokens": "<< Creating summaries >> on [[ lengthy Semantic Web documents ]] for quick identification of the corresponding entity has been of great contemporary interest .", "h": ["lengthy Semantic Web documents"], "t": ["Creating summaries"]}, {"label": "FEATURE-OF", "tokens": "Specifically , we highlight the importance of << diversified -LRB- faceted -RRB- summaries >> by combining three dimensions : [[ diversity ]] , uniqueness , and popularity .", "h": ["diversity"], "t": ["diversified -LRB- faceted -RRB- summaries"]}, {"label": "CONJUNCTION", "tokens": "Specifically , we highlight the importance of diversified -LRB- faceted -RRB- summaries by combining three dimensions : [[ diversity ]] , << uniqueness >> , and popularity .", "h": ["diversity"], "t": ["uniqueness"]}, {"label": "FEATURE-OF", "tokens": "Specifically , we highlight the importance of << diversified -LRB- faceted -RRB- summaries >> by combining three dimensions : diversity , [[ uniqueness ]] , and popularity .", "h": ["uniqueness"], "t": ["diversified -LRB- faceted -RRB- summaries"]}, {"label": "CONJUNCTION", "tokens": "Specifically , we highlight the importance of diversified -LRB- faceted -RRB- summaries by combining three dimensions : diversity , [[ uniqueness ]] , and << popularity >> .", "h": ["uniqueness"], "t": ["popularity"]}, {"label": "FEATURE-OF", "tokens": "Specifically , we highlight the importance of << diversified -LRB- faceted -RRB- summaries >> by combining three dimensions : diversity , uniqueness , and [[ popularity ]] .", "h": ["popularity"], "t": ["diversified -LRB- faceted -RRB- summaries"]}, {"label": "USED-FOR", "tokens": "Our novel << diversity-aware entity summarization approach >> mimics [[ human conceptual clustering techniques ]] to group facts , and picks representative facts from each group to form concise -LRB- i.e. , short -RRB- and comprehensive -LRB- i.e. , improved coverage through diversity -RRB- summaries .", "h": ["human conceptual clustering techniques"], "t": ["diversity-aware entity summarization approach"]}, {"label": "USED-FOR", "tokens": "We evaluate our [[ approach ]] against the state-of-the-art techniques and show that our work improves both the quality and the efficiency of << entity summarization >> .", "h": ["approach"], "t": ["entity summarization"]}, {"label": "COMPARE", "tokens": "We evaluate our << approach >> against the [[ state-of-the-art techniques ]] and show that our work improves both the quality and the efficiency of entity summarization .", "h": ["state-of-the-art techniques"], "t": ["approach"]}, {"label": "USED-FOR", "tokens": "We evaluate our approach against the [[ state-of-the-art techniques ]] and show that our work improves both the quality and the efficiency of << entity summarization >> .", "h": ["state-of-the-art techniques"], "t": ["entity summarization"]}, {"label": "EVALUATE-FOR", "tokens": "We evaluate our approach against the state-of-the-art techniques and show that our work improves both the [[ quality ]] and the efficiency of << entity summarization >> .", "h": ["quality"], "t": ["entity summarization"]}, {"label": "EVALUATE-FOR", "tokens": "We evaluate our approach against the state-of-the-art techniques and show that our work improves both the quality and the [[ efficiency ]] of << entity summarization >> .", "h": ["efficiency"], "t": ["entity summarization"]}, {"label": "USED-FOR", "tokens": "We present a [[ framework ]] for the << fast computation of lexical affinity models >> .", "h": ["framework"], "t": ["fast computation of lexical affinity models"]}, {"label": "PART-OF", "tokens": "The << framework >> is composed of a novel [[ algorithm ]] to efficiently compute the co-occurrence distribution between pairs of terms , an independence model , and a parametric affinity model .", "h": ["algorithm"], "t": ["framework"]}, {"label": "USED-FOR", "tokens": "The framework is composed of a novel [[ algorithm ]] to efficiently compute the << co-occurrence distribution >> between pairs of terms , an independence model , and a parametric affinity model .", "h": ["algorithm"], "t": ["co-occurrence distribution"]}, {"label": "CONJUNCTION", "tokens": "The framework is composed of a novel [[ algorithm ]] to efficiently compute the co-occurrence distribution between pairs of terms , an << independence model >> , and a parametric affinity model .", "h": ["algorithm"], "t": ["independence model"]}, {"label": "PART-OF", "tokens": "The << framework >> is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms , an [[ independence model ]] , and a parametric affinity model .", "h": ["independence model"], "t": ["framework"]}, {"label": "CONJUNCTION", "tokens": "The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms , an [[ independence model ]] , and a << parametric affinity model >> .", "h": ["independence model"], "t": ["parametric affinity model"]}, {"label": "PART-OF", "tokens": "The << framework >> is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms , an independence model , and a [[ parametric affinity model ]] .", "h": ["parametric affinity model"], "t": ["framework"]}, {"label": "USED-FOR", "tokens": "In comparison with previous models , which either use arbitrary windows to compute similarity between words or use [[ lexical affinity ]] to create << sequential models >> , in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus .", "h": ["lexical affinity"], "t": ["sequential models"]}, {"label": "COMPARE", "tokens": "In comparison with previous << models >> , which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models , in this paper we focus on [[ models ]] intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus .", "h": ["models"], "t": ["models"]}, {"label": "USED-FOR", "tokens": "In comparison with previous models , which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models , in this paper we focus on [[ models ]] intended to capture the << co-occurrence patterns >> of any pair of words or phrases at any distance in the corpus .", "h": ["models"], "t": ["co-occurrence patterns"]}, {"label": "USED-FOR", "tokens": "We apply [[ it ]] in combination with a terabyte corpus to answer << natural language tests >> , achieving encouraging results .", "h": ["it"], "t": ["natural language tests"]}, {"label": "EVALUATE-FOR", "tokens": "We apply << it >> in combination with a [[ terabyte corpus ]] to answer natural language tests , achieving encouraging results .", "h": ["terabyte corpus"], "t": ["it"]}, {"label": "USED-FOR", "tokens": "This paper introduces a [[ system ]] for << categorizing unknown words >> .", "h": ["system"], "t": ["categorizing unknown words"]}, {"label": "USED-FOR", "tokens": "The << system >> is based on a [[ multi-component architecture ]] where each component is responsible for identifying one class of unknown words .", "h": ["multi-component architecture"], "t": ["system"]}, {"label": "PART-OF", "tokens": "The system is based on a << multi-component architecture >> where each [[ component ]] is responsible for identifying one class of unknown words .", "h": ["component"], "t": ["multi-component architecture"]}, {"label": "USED-FOR", "tokens": "The system is based on a multi-component architecture where each [[ component ]] is responsible for identifying one class of << unknown words >> .", "h": ["component"], "t": ["unknown words"]}, {"label": "USED-FOR", "tokens": "The focus of this paper is the [[ components ]] that identify << names >> and spelling errors .", "h": ["components"], "t": ["names"]}, {"label": "USED-FOR", "tokens": "The focus of this paper is the [[ components ]] that identify names and << spelling errors >> .", "h": ["components"], "t": ["spelling errors"]}, {"label": "CONJUNCTION", "tokens": "The focus of this paper is the components that identify [[ names ]] and << spelling errors >> .", "h": ["names"], "t": ["spelling errors"]}, {"label": "USED-FOR", "tokens": "Each << component >> uses a [[ decision tree architecture ]] to combine multiple types of evidence about the unknown word .", "h": ["decision tree architecture"], "t": ["component"]}, {"label": "EVALUATE-FOR", "tokens": "The << system >> is evaluated using data from [[ live closed captions ]] - a genre replete with a wide variety of unknown words .", "h": ["live closed captions"], "t": ["system"]}, {"label": "HYPONYM-OF", "tokens": "At MIT Lincoln Laboratory , we have been developing a << Korean-to-English machine translation system >> [[ CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB- ]] .", "h": ["CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB-"], "t": ["Korean-to-English machine translation system"]}, {"label": "PART-OF", "tokens": "The << CCLINC Korean-to-English translation system >> consists of two [[ core modules ]] , language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame .", "h": ["core modules"], "t": ["CCLINC Korean-to-English translation system"]}, {"label": "USED-FOR", "tokens": "The CCLINC Korean-to-English translation system consists of two core modules , << language understanding and generation modules >> mediated by a [[ language neutral meaning representation ]] called a semantic frame .", "h": ["language neutral meaning representation"], "t": ["language understanding and generation modules"]}, {"label": "HYPONYM-OF", "tokens": "The CCLINC Korean-to-English translation system consists of two core modules , language understanding and generation modules mediated by a << language neutral meaning representation >> called a [[ semantic frame ]] .", "h": ["semantic frame"], "t": ["language neutral meaning representation"]}, {"label": "HYPONYM-OF", "tokens": "The key features of the system include : -LRB- i -RRB- Robust efficient parsing of [[ Korean ]] -LRB- a << verb final language >> with overt case markers , relatively free word order , and frequent omissions of arguments -RRB- .", "h": ["Korean"], "t": ["verb final language"]}, {"label": "FEATURE-OF", "tokens": "The key features of the system include : -LRB- i -RRB- Robust efficient parsing of Korean -LRB- a << verb final language >> with [[ overt case markers ]] , relatively free word order , and frequent omissions of arguments -RRB- .", "h": ["overt case markers"], "t": ["verb final language"]}, {"label": "USED-FOR", "tokens": "-LRB- ii -RRB- High quality << translation >> via [[ word sense disambiguation ]] and accurate word order generation of the target language .", "h": ["word sense disambiguation"], "t": ["translation"]}, {"label": "CONJUNCTION", "tokens": "-LRB- ii -RRB- High quality translation via [[ word sense disambiguation ]] and accurate << word order generation >> of the target language .", "h": ["word sense disambiguation"], "t": ["word order generation"]}, {"label": "USED-FOR", "tokens": "-LRB- ii -RRB- High quality << translation >> via word sense disambiguation and accurate [[ word order generation ]] of the target language .", "h": ["word order generation"], "t": ["translation"]}, {"label": "USED-FOR", "tokens": "Having been trained on [[ Korean newspaper articles ]] on missiles and chemical biological warfare , the << system >> produces the translation output sufficient for content understanding of the original document .", "h": ["Korean newspaper articles"], "t": ["system"]}, {"label": "FEATURE-OF", "tokens": "Having been trained on << Korean newspaper articles >> on [[ missiles and chemical biological warfare ]] , the system produces the translation output sufficient for content understanding of the original document .", "h": ["missiles and chemical biological warfare"], "t": ["Korean newspaper articles"]}, {"label": "USED-FOR", "tokens": "The [[ JAVELIN system ]] integrates a flexible , planning-based architecture with a variety of language processing modules to provide an << open-domain question answering capability >> on free text .", "h": ["JAVELIN system"], "t": ["open-domain question answering capability"]}, {"label": "PART-OF", "tokens": "The << JAVELIN system >> integrates a flexible , [[ planning-based architecture ]] with a variety of language processing modules to provide an open-domain question answering capability on free text .", "h": ["planning-based architecture"], "t": ["JAVELIN system"]}, {"label": "PART-OF", "tokens": "The << JAVELIN system >> integrates a flexible , planning-based architecture with a variety of [[ language processing modules ]] to provide an open-domain question answering capability on free text .", "h": ["language processing modules"], "t": ["JAVELIN system"]}, {"label": "CONJUNCTION", "tokens": "The JAVELIN system integrates a flexible , << planning-based architecture >> with a variety of [[ language processing modules ]] to provide an open-domain question answering capability on free text .", "h": ["language processing modules"], "t": ["planning-based architecture"]}, {"label": "USED-FOR", "tokens": "We present the first application of the [[ head-driven statistical parsing model ]] of Collins -LRB- 1999 -RRB- as a << simultaneous language model >> and parser for large-vocabulary speech recognition .", "h": ["head-driven statistical parsing model"], "t": ["simultaneous language model"]}, {"label": "USED-FOR", "tokens": "We present the first application of the [[ head-driven statistical parsing model ]] of Collins -LRB- 1999 -RRB- as a simultaneous language model and << parser >> for large-vocabulary speech recognition .", "h": ["head-driven statistical parsing model"], "t": ["parser"]}, {"label": "CONJUNCTION", "tokens": "We present the first application of the head-driven statistical parsing model of Collins -LRB- 1999 -RRB- as a [[ simultaneous language model ]] and << parser >> for large-vocabulary speech recognition .", "h": ["simultaneous language model"], "t": ["parser"]}, {"label": "USED-FOR", "tokens": "We present the first application of the head-driven statistical parsing model of Collins -LRB- 1999 -RRB- as a [[ simultaneous language model ]] and parser for << large-vocabulary speech recognition >> .", "h": ["simultaneous language model"], "t": ["large-vocabulary speech recognition"]}, {"label": "USED-FOR", "tokens": "We present the first application of the head-driven statistical parsing model of Collins -LRB- 1999 -RRB- as a simultaneous language model and [[ parser ]] for << large-vocabulary speech recognition >> .", "h": ["parser"], "t": ["large-vocabulary speech recognition"]}, {"label": "USED-FOR", "tokens": "The [[ model ]] is adapted to an << online left to right chart-parser >> for word lattices , integrating acoustic , n-gram , and parser probabilities .", "h": ["model"], "t": ["online left to right chart-parser"]}, {"label": "USED-FOR", "tokens": "The model is adapted to an [[ online left to right chart-parser ]] for << word lattices >> , integrating acoustic , n-gram , and parser probabilities .", "h": ["online left to right chart-parser"], "t": ["word lattices"]}, {"label": "PART-OF", "tokens": "The model is adapted to an << online left to right chart-parser >> for word lattices , integrating [[ acoustic , n-gram , and parser probabilities ]] .", "h": ["acoustic , n-gram , and parser probabilities"], "t": ["online left to right chart-parser"]}, {"label": "USED-FOR", "tokens": "The << parser >> uses [[ structural and lexical dependencies ]] not considered by n-gram models , conditioning recognition on more linguistically-grounded relationships .", "h": ["structural and lexical dependencies"], "t": ["parser"]}, {"label": "CONJUNCTION", "tokens": "Experiments on the [[ Wall Street Journal treebank ]] and << lattice corpora >> show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding .", "h": ["Wall Street Journal treebank"], "t": ["lattice corpora"]}, {"label": "EVALUATE-FOR", "tokens": "Experiments on the [[ Wall Street Journal treebank ]] and lattice corpora show word error rates competitive with the standard << n-gram language model >> while extracting additional structural information useful for speech understanding .", "h": ["Wall Street Journal treebank"], "t": ["n-gram language model"]}, {"label": "EVALUATE-FOR", "tokens": "Experiments on the Wall Street Journal treebank and [[ lattice corpora ]] show word error rates competitive with the standard << n-gram language model >> while extracting additional structural information useful for speech understanding .", "h": ["lattice corpora"], "t": ["n-gram language model"]}, {"label": "EVALUATE-FOR", "tokens": "Experiments on the Wall Street Journal treebank and lattice corpora show [[ word error rates ]] competitive with the standard << n-gram language model >> while extracting additional structural information useful for speech understanding .", "h": ["word error rates"], "t": ["n-gram language model"]}, {"label": "USED-FOR", "tokens": "Experiments on the Wall Street Journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional [[ structural information ]] useful for << speech understanding >> .", "h": ["structural information"], "t": ["speech understanding"]}, {"label": "PART-OF", "tokens": "[[ Image composition -LRB- or mosaicing -RRB- ]] has attracted a growing attention in recent years as one of the main elements in << video analysis and representation >> .", "h": ["Image composition -LRB- or mosaicing -RRB-"], "t": ["video analysis and representation"]}, {"label": "CONJUNCTION", "tokens": "In this paper we deal with the problem of [[ global alignment ]] and << super-resolution >> .", "h": ["global alignment"], "t": ["super-resolution"]}, {"label": "EVALUATE-FOR", "tokens": "We also propose to evaluate the quality of the resulting << mosaic >> by measuring the [[ amount of blurring ]] .", "h": ["amount of blurring"], "t": ["mosaic"]}, {"label": "USED-FOR", "tokens": "<< Global registration >> is achieved by combining a [[ graph-based technique ]] -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a bundle adjustment which uses only the homographies computed in the previous steps .", "h": ["graph-based technique"], "t": ["Global registration"]}, {"label": "USED-FOR", "tokens": "Global registration is achieved by combining a [[ graph-based technique ]] -- that exploits the << topological structure >> of the sequence induced by the spatial overlap -- with a bundle adjustment which uses only the homographies computed in the previous steps .", "h": ["graph-based technique"], "t": ["topological structure"]}, {"label": "CONJUNCTION", "tokens": "Global registration is achieved by combining a [[ graph-based technique ]] -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a << bundle adjustment >> which uses only the homographies computed in the previous steps .", "h": ["graph-based technique"], "t": ["bundle adjustment"]}, {"label": "USED-FOR", "tokens": "<< Global registration >> is achieved by combining a graph-based technique -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a [[ bundle adjustment ]] which uses only the homographies computed in the previous steps .", "h": ["bundle adjustment"], "t": ["Global registration"]}, {"label": "USED-FOR", "tokens": "Global registration is achieved by combining a graph-based technique -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a << bundle adjustment >> which uses only the [[ homographies ]] computed in the previous steps .", "h": ["homographies"], "t": ["bundle adjustment"]}, {"label": "COMPARE", "tokens": "Experimental comparison with other << techniques >> shows the effectiveness of our [[ approach ]] .", "h": ["approach"], "t": ["techniques"]}, {"label": "USED-FOR", "tokens": "The main of this project is << computer-assisted acquisition and morpho-syntactic description of verb-noun collocations >> in [[ Polish ]] .", "h": ["Polish"], "t": ["computer-assisted acquisition and morpho-syntactic description of verb-noun collocations"]}, {"label": "HYPONYM-OF", "tokens": "We present methodology and resources obtained in three main project << phases >> which are : [[ dictionary-based acquisition of collocation lexicon ]] , feasibility study for corpus-based lexicon enlargement phase , corpus-based lexicon enlargement and collocation description .", "h": ["dictionary-based acquisition of collocation lexicon"], "t": ["phases"]}, {"label": "CONJUNCTION", "tokens": "We present methodology and resources obtained in three main project phases which are : [[ dictionary-based acquisition of collocation lexicon ]] , << feasibility study >> for corpus-based lexicon enlargement phase , corpus-based lexicon enlargement and collocation description .", "h": ["dictionary-based acquisition of collocation lexicon"], "t": ["feasibility study"]}, {"label": "HYPONYM-OF", "tokens": "We present methodology and resources obtained in three main project << phases >> which are : dictionary-based acquisition of collocation lexicon , [[ feasibility study ]] for corpus-based lexicon enlargement phase , corpus-based lexicon enlargement and collocation description .", "h": ["feasibility study"], "t": ["phases"]}, {"label": "USED-FOR", "tokens": "We present methodology and resources obtained in three main project phases which are : dictionary-based acquisition of collocation lexicon , [[ feasibility study ]] for << corpus-based lexicon enlargement phase >> , corpus-based lexicon enlargement and collocation description .", "h": ["feasibility study"], "t": ["corpus-based lexicon enlargement phase"]}, {"label": "HYPONYM-OF", "tokens": "We present methodology and resources obtained in three main project << phases >> which are : dictionary-based acquisition of collocation lexicon , feasibility study for corpus-based lexicon enlargement phase , [[ corpus-based lexicon enlargement and collocation description ]] .", "h": ["corpus-based lexicon enlargement and collocation description"], "t": ["phases"]}, {"label": "CONJUNCTION", "tokens": "We present methodology and resources obtained in three main project phases which are : dictionary-based acquisition of collocation lexicon , << feasibility study >> for corpus-based lexicon enlargement phase , [[ corpus-based lexicon enlargement and collocation description ]] .", "h": ["corpus-based lexicon enlargement and collocation description"], "t": ["feasibility study"]}, {"label": "USED-FOR", "tokens": "The presented here [[ corpus-based approach ]] permitted us to triple the size the << verb-noun collocation dictionary >> for Polish .", "h": ["corpus-based approach"], "t": ["verb-noun collocation dictionary"]}, {"label": "FEATURE-OF", "tokens": "The presented here corpus-based approach permitted us to triple the size the << verb-noun collocation dictionary >> for [[ Polish ]] .", "h": ["Polish"], "t": ["verb-noun collocation dictionary"]}, {"label": "USED-FOR", "tokens": "Along with the increasing requirements , the [[ hash-tag recommendation task ]] for << microblogs >> has been receiving considerable attention in recent years .", "h": ["hash-tag recommendation task"], "t": ["microblogs"]}, {"label": "USED-FOR", "tokens": "Motivated by the successful use of [[ convolutional neural networks -LRB- CNNs -RRB- ]] for many << natural language processing tasks >> , in this paper , we adopt CNNs to perform the hashtag recommendation problem .", "h": ["convolutional neural networks -LRB- CNNs -RRB-"], "t": ["natural language processing tasks"]}, {"label": "USED-FOR", "tokens": "To incorporate the << trigger words >> whose effectiveness have been experimentally evaluated in several previous works , we propose a novel [[ architecture ]] with an attention mechanism .", "h": ["architecture"], "t": ["trigger words"]}, {"label": "FEATURE-OF", "tokens": "To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works , we propose a novel << architecture >> with an [[ attention mechanism ]] .", "h": ["attention mechanism"], "t": ["architecture"]}, {"label": "EVALUATE-FOR", "tokens": "The results of experiments on the [[ data ]] collected from a real world microblogging service demonstrated that the proposed << model >> outperforms state-of-the-art methods .", "h": ["data"], "t": ["model"]}, {"label": "COMPARE", "tokens": "The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed [[ model ]] outperforms << state-of-the-art methods >> .", "h": ["model"], "t": ["state-of-the-art methods"]}, {"label": "COMPARE", "tokens": "By incorporating trigger words into the consideration , the relative improvement of the proposed [[ method ]] over the << state-of-the-art method >> is around 9.4 % in the F1-score .", "h": ["method"], "t": ["state-of-the-art method"]}, {"label": "EVALUATE-FOR", "tokens": "By incorporating trigger words into the consideration , the relative improvement of the proposed method over the << state-of-the-art method >> is around 9.4 % in the [[ F1-score ]] .", "h": ["F1-score"], "t": ["state-of-the-art method"]}, {"label": "USED-FOR", "tokens": "In this paper , we improve an << unsupervised learning method >> using the [[ Expectation-Maximization -LRB- EM -RRB- algorithm ]] proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation -LRB- WSD -RRB- problems .", "h": ["Expectation-Maximization -LRB- EM -RRB- algorithm"], "t": ["unsupervised learning method"]}, {"label": "USED-FOR", "tokens": "In this paper , we improve an unsupervised learning method using the [[ Expectation-Maximization -LRB- EM -RRB- algorithm ]] proposed by Nigam et al. for << text classification problems >> in order to apply it to word sense disambiguation -LRB- WSD -RRB- problems .", "h": ["Expectation-Maximization -LRB- EM -RRB- algorithm"], "t": ["text classification problems"]}, {"label": "USED-FOR", "tokens": "In this paper , we improve an unsupervised learning method using the Expectation-Maximization -LRB- EM -RRB- algorithm proposed by Nigam et al. for text classification problems in order to apply [[ it ]] to << word sense disambiguation -LRB- WSD -RRB- problems >> .", "h": ["it"], "t": ["word sense disambiguation -LRB- WSD -RRB- problems"]}, {"label": "FEATURE-OF", "tokens": "In experiments , we solved 50 noun WSD problems in the [[ Japanese Dictionary Task ]] in << SENSEVAL2 >> .", "h": ["Japanese Dictionary Task"], "t": ["SENSEVAL2"]}, {"label": "USED-FOR", "tokens": "Furthermore , our [[ methods ]] were confirmed to be effective also for << verb WSD problems >> .", "h": ["methods"], "t": ["verb WSD problems"]}, {"label": "USED-FOR", "tokens": "[[ Dividing sentences in chunks of words ]] is a useful preprocessing step for << parsing >> , information extraction and information retrieval .", "h": ["Dividing sentences in chunks of words"], "t": ["parsing"]}, {"label": "USED-FOR", "tokens": "[[ Dividing sentences in chunks of words ]] is a useful preprocessing step for parsing , << information extraction >> and information retrieval .", "h": ["Dividing sentences in chunks of words"], "t": ["information extraction"]}, {"label": "USED-FOR", "tokens": "[[ Dividing sentences in chunks of words ]] is a useful preprocessing step for parsing , information extraction and << information retrieval >> .", "h": ["Dividing sentences in chunks of words"], "t": ["information retrieval"]}, {"label": "CONJUNCTION", "tokens": "Dividing sentences in chunks of words is a useful preprocessing step for [[ parsing ]] , << information extraction >> and information retrieval .", "h": ["parsing"], "t": ["information extraction"]}, {"label": "CONJUNCTION", "tokens": "Dividing sentences in chunks of words is a useful preprocessing step for parsing , [[ information extraction ]] and << information retrieval >> .", "h": ["information extraction"], "t": ["information retrieval"]}, {"label": "USED-FOR", "tokens": "-LRB- Ramshaw and Marcus , 1995 -RRB- have introduced a `` convenient '' [[ data representation ]] for << chunking >> by converting it to a tagging task .", "h": ["data representation"], "t": ["chunking"]}, {"label": "USED-FOR", "tokens": "In this paper we will examine seven different [[ data representations ]] for the problem of << recognizing noun phrase chunks >> .", "h": ["data representations"], "t": ["recognizing noun phrase chunks"]}, {"label": "USED-FOR", "tokens": "However , equipped with the most suitable [[ data representation ]] , our << memory-based learning chunker >> was able to improve the best published chunking results for a standard data set .", "h": ["data representation"], "t": ["memory-based learning chunker"]}, {"label": "EVALUATE-FOR", "tokens": "However , equipped with the most suitable data representation , our << memory-based learning chunker >> was able to improve the best published chunking results for a standard [[ data set ]] .", "h": ["data set"], "t": ["memory-based learning chunker"]}, {"label": "USED-FOR", "tokens": "We focus on << FAQ-like questions and answers >> , and build our [[ system ]] around a noisy-channel architecture which exploits both a language model for answers and a transformation model for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .", "h": ["system"], "t": ["FAQ-like questions and answers"]}, {"label": "USED-FOR", "tokens": "We focus on FAQ-like questions and answers , and build our << system >> around a [[ noisy-channel architecture ]] which exploits both a language model for answers and a transformation model for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .", "h": ["noisy-channel architecture"], "t": ["system"]}, {"label": "USED-FOR", "tokens": "We focus on FAQ-like questions and answers , and build our system around a [[ noisy-channel architecture ]] which exploits both a << language model >> for answers and a transformation model for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .", "h": ["noisy-channel architecture"], "t": ["language model"]}, {"label": "USED-FOR", "tokens": "We focus on FAQ-like questions and answers , and build our system around a [[ noisy-channel architecture ]] which exploits both a language model for answers and a << transformation model >> for answer/question terms , trained on a corpus of 1 million question/answer pairs collected from the Web .", "h": ["noisy-channel architecture"], "t": ["transformation model"]}, {"label": "EVALUATE-FOR", "tokens": "In this paper we evaluate four objective [[ measures of speech ]] with regards to << intelligibility prediction >> of synthesized speech in diverse noisy situations .", "h": ["measures of speech"], "t": ["intelligibility prediction"]}, {"label": "USED-FOR", "tokens": "In this paper we evaluate four objective measures of speech with regards to << intelligibility prediction >> of [[ synthesized speech ]] in diverse noisy situations .", "h": ["synthesized speech"], "t": ["intelligibility prediction"]}, {"label": "FEATURE-OF", "tokens": "In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of << synthesized speech >> in [[ diverse noisy situations ]] .", "h": ["diverse noisy situations"], "t": ["synthesized speech"]}, {"label": "CONJUNCTION", "tokens": "We evaluated three [[ intel-ligibility measures ]] , the Dau measure , the glimpse proportion and the Speech Intelligibility Index -LRB- SII -RRB- and a << quality measure >> , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["intel-ligibility measures"], "t": ["quality measure"]}, {"label": "HYPONYM-OF", "tokens": "We evaluated three << intel-ligibility measures >> , the [[ Dau measure ]] , the glimpse proportion and the Speech Intelligibility Index -LRB- SII -RRB- and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["Dau measure"], "t": ["intel-ligibility measures"]}, {"label": "CONJUNCTION", "tokens": "We evaluated three intel-ligibility measures , the [[ Dau measure ]] , the << glimpse proportion >> and the Speech Intelligibility Index -LRB- SII -RRB- and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["Dau measure"], "t": ["glimpse proportion"]}, {"label": "HYPONYM-OF", "tokens": "We evaluated three << intel-ligibility measures >> , the Dau measure , the [[ glimpse proportion ]] and the Speech Intelligibility Index -LRB- SII -RRB- and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["glimpse proportion"], "t": ["intel-ligibility measures"]}, {"label": "CONJUNCTION", "tokens": "We evaluated three intel-ligibility measures , the Dau measure , the [[ glimpse proportion ]] and the << Speech Intelligibility Index -LRB- SII -RRB- >> and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["glimpse proportion"], "t": ["Speech Intelligibility Index -LRB- SII -RRB-"]}, {"label": "HYPONYM-OF", "tokens": "We evaluated three << intel-ligibility measures >> , the Dau measure , the glimpse proportion and the [[ Speech Intelligibility Index -LRB- SII -RRB- ]] and a quality measure , the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- .", "h": ["Speech Intelligibility Index -LRB- SII -RRB-"], "t": ["intel-ligibility measures"]}, {"label": "HYPONYM-OF", "tokens": "We evaluated three intel-ligibility measures , the Dau measure , the glimpse proportion and the Speech Intelligibility Index -LRB- SII -RRB- and a << quality measure >> , the [[ Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB- ]] .", "h": ["Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB-"], "t": ["quality measure"]}, {"label": "USED-FOR", "tokens": "For the << generation of synthesized speech >> we used a state of the art [[ HMM-based speech synthesis system ]] .", "h": ["HMM-based speech synthesis system"], "t": ["generation of synthesized speech"]}, {"label": "PART-OF", "tokens": "The << noisy conditions >> comprised four [[ additive noises ]] .", "h": ["additive noises"], "t": ["noisy conditions"]}, {"label": "COMPARE", "tokens": "The [[ measures ]] were compared with << subjective intelligibility scores >> obtained in listening tests .", "h": ["measures"], "t": ["subjective intelligibility scores"]}, {"label": "CONJUNCTION", "tokens": "The results show the [[ Dau ]] and the << glimpse measures >> to be the best predictors of intelligibility , with correlations of around 0.83 to subjective scores .", "h": ["Dau"], "t": ["glimpse measures"]}, {"label": "HYPONYM-OF", "tokens": "The results show the [[ Dau ]] and the glimpse measures to be the best << predictors of intelligibility >> , with correlations of around 0.83 to subjective scores .", "h": ["Dau"], "t": ["predictors of intelligibility"]}, {"label": "COMPARE", "tokens": "The results show the [[ Dau ]] and the glimpse measures to be the best predictors of intelligibility , with correlations of around 0.83 to << subjective scores >> .", "h": ["Dau"], "t": ["subjective scores"]}, {"label": "HYPONYM-OF", "tokens": "The results show the Dau and the [[ glimpse measures ]] to be the best << predictors of intelligibility >> , with correlations of around 0.83 to subjective scores .", "h": ["glimpse measures"], "t": ["predictors of intelligibility"]}, {"label": "COMPARE", "tokens": "The results show the Dau and the [[ glimpse measures ]] to be the best predictors of intelligibility , with correlations of around 0.83 to << subjective scores >> .", "h": ["glimpse measures"], "t": ["subjective scores"]}, {"label": "EVALUATE-FOR", "tokens": "The results show the << Dau >> and the glimpse measures to be the best predictors of intelligibility , with [[ correlations ]] of around 0.83 to subjective scores .", "h": ["correlations"], "t": ["Dau"]}, {"label": "EVALUATE-FOR", "tokens": "The results show the Dau and the << glimpse measures >> to be the best predictors of intelligibility , with [[ correlations ]] of around 0.83 to subjective scores .", "h": ["correlations"], "t": ["glimpse measures"]}, {"label": "EVALUATE-FOR", "tokens": "All [[ measures ]] gave less accurate << predictions of intelligibility >> for synthetic speech than have previously been found for natural speech ; in particular the SII measure .", "h": ["measures"], "t": ["predictions of intelligibility"]}, {"label": "USED-FOR", "tokens": "All measures gave less accurate << predictions of intelligibility >> for [[ synthetic speech ]] than have previously been found for natural speech ; in particular the SII measure .", "h": ["synthetic speech"], "t": ["predictions of intelligibility"]}, {"label": "COMPARE", "tokens": "All measures gave less accurate predictions of intelligibility for [[ synthetic speech ]] than have previously been found for << natural speech >> ; in particular the SII measure .", "h": ["synthetic speech"], "t": ["natural speech"]}, {"label": "HYPONYM-OF", "tokens": "All << measures >> gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech ; in particular the [[ SII measure ]] .", "h": ["SII measure"], "t": ["measures"]}, {"label": "USED-FOR", "tokens": "In additional experiments , we processed the << synthesized speech >> by an [[ ideal binary mask ]] before adding noise .", "h": ["ideal binary mask"], "t": ["synthesized speech"]}, {"label": "USED-FOR", "tokens": "The [[ Glimpse measure ]] gave the most accurate << intelligibility predictions >> in this situation .", "h": ["Glimpse measure"], "t": ["intelligibility predictions"]}, {"label": "USED-FOR", "tokens": "A [[ '' graphics for vision '' approach ]] is proposed to address the problem of << reconstruction >> from a large and imperfect data set : reconstruction on demand by tensor voting , or ROD-TV .", "h": ["'' graphics for vision '' approach"], "t": ["reconstruction"]}, {"label": "USED-FOR", "tokens": "A '' graphics for vision '' approach is proposed to address the problem of << reconstruction >> from a [[ large and imperfect data set ]] : reconstruction on demand by tensor voting , or ROD-TV .", "h": ["large and imperfect data set"], "t": ["reconstruction"]}, {"label": "USED-FOR", "tokens": "A '' graphics for vision '' approach is proposed to address the problem of reconstruction from a large and imperfect data set : << reconstruction >> on demand by [[ tensor voting ]] , or ROD-TV .", "h": ["tensor voting"], "t": ["reconstruction"]}, {"label": "CONJUNCTION", "tokens": "A '' graphics for vision '' approach is proposed to address the problem of reconstruction from a large and imperfect data set : reconstruction on demand by [[ tensor voting ]] , or << ROD-TV >> .", "h": ["tensor voting"], "t": ["ROD-TV"]}, {"label": "USED-FOR", "tokens": "A '' graphics for vision '' approach is proposed to address the problem of reconstruction from a large and imperfect data set : << reconstruction >> on demand by tensor voting , or [[ ROD-TV ]] .", "h": ["ROD-TV"], "t": ["reconstruction"]}, {"label": "EVALUATE-FOR", "tokens": "<< ROD-TV >> simultaneously delivers good [[ efficiency ]] and robust-ness , by adapting to a continuum of primitive connectivity , view dependence , and levels of detail -LRB- LOD -RRB- .", "h": ["efficiency"], "t": ["ROD-TV"]}, {"label": "EVALUATE-FOR", "tokens": "<< ROD-TV >> simultaneously delivers good efficiency and [[ robust-ness ]] , by adapting to a continuum of primitive connectivity , view dependence , and levels of detail -LRB- LOD -RRB- .", "h": ["robust-ness"], "t": ["ROD-TV"]}, {"label": "CONJUNCTION", "tokens": "ROD-TV simultaneously delivers good << efficiency >> and [[ robust-ness ]] , by adapting to a continuum of primitive connectivity , view dependence , and levels of detail -LRB- LOD -RRB- .", "h": ["robust-ness"], "t": ["efficiency"]}, {"label": "CONJUNCTION", "tokens": "ROD-TV simultaneously delivers good efficiency and robust-ness , by adapting to a continuum of << primitive connectivity >> , [[ view dependence ]] , and levels of detail -LRB- LOD -RRB- .", "h": ["view dependence"], "t": ["primitive connectivity"]}, {"label": "CONJUNCTION", "tokens": "ROD-TV simultaneously delivers good efficiency and robust-ness , by adapting to a continuum of primitive connectivity , << view dependence >> , and [[ levels of detail -LRB- LOD -RRB- ]] .", "h": ["levels of detail -LRB- LOD -RRB-"], "t": ["view dependence"]}, {"label": "USED-FOR", "tokens": "[[ Locally inferred surface elements ]] are robust to noise and better capture << local shapes >> .", "h": ["Locally inferred surface elements"], "t": ["local shapes"]}, {"label": "USED-FOR", "tokens": "By inferring [[ per-vertex normals ]] at sub-voxel precision on the fly , we can achieve << interpolative shading >> .", "h": ["per-vertex normals"], "t": ["interpolative shading"]}, {"label": "FEATURE-OF", "tokens": "By inferring << per-vertex normals >> at [[ sub-voxel precision ]] on the fly , we can achieve interpolative shading .", "h": ["sub-voxel precision"], "t": ["per-vertex normals"]}, {"label": "USED-FOR", "tokens": "By relaxing the [[ mesh connectivity requirement ]] , we extend ROD-TV and propose a simple but effective << multiscale feature extraction algorithm >> .", "h": ["mesh connectivity requirement"], "t": ["multiscale feature extraction algorithm"]}, {"label": "USED-FOR", "tokens": "By relaxing the mesh connectivity requirement , we extend [[ ROD-TV ]] and propose a simple but effective << multiscale feature extraction algorithm >> .", "h": ["ROD-TV"], "t": ["multiscale feature extraction algorithm"]}, {"label": "PART-OF", "tokens": "<< ROD-TV >> consists of a [[ hierarchical data structure ]] that encodes different levels of detail .", "h": ["hierarchical data structure"], "t": ["ROD-TV"]}, {"label": "HYPONYM-OF", "tokens": "The << local reconstruction algorithm >> is [[ tensor voting ]] .", "h": ["tensor voting"], "t": ["local reconstruction algorithm"]}, {"label": "USED-FOR", "tokens": "<< It >> is applied on demand to the visible subset of data at a desired level of detail , by [[ traversing the data hierarchy ]] and collecting tensorial support in a neighborhood .", "h": ["traversing the data hierarchy"], "t": ["It"]}, {"label": "CONJUNCTION", "tokens": "It is applied on demand to the visible subset of data at a desired level of detail , by [[ traversing the data hierarchy ]] and << collecting tensorial support >> in a neighborhood .", "h": ["traversing the data hierarchy"], "t": ["collecting tensorial support"]}, {"label": "USED-FOR", "tokens": "<< It >> is applied on demand to the visible subset of data at a desired level of detail , by traversing the data hierarchy and [[ collecting tensorial support ]] in a neighborhood .", "h": ["collecting tensorial support"], "t": ["It"]}, {"label": "CONJUNCTION", "tokens": "Both [[ rhetorical structure ]] and << punctuation >> have been helpful in discourse processing .", "h": ["rhetorical structure"], "t": ["punctuation"]}, {"label": "USED-FOR", "tokens": "Both [[ rhetorical structure ]] and punctuation have been helpful in << discourse processing >> .", "h": ["rhetorical structure"], "t": ["discourse processing"]}, {"label": "USED-FOR", "tokens": "Both rhetorical structure and [[ punctuation ]] have been helpful in << discourse processing >> .", "h": ["punctuation"], "t": ["discourse processing"]}, {"label": "PART-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 [[ Chinese punctuation marks ]] in << news commentary texts >> : Colon , Dash , Ellipsis , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Chinese punctuation marks"], "t": ["news commentary texts"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : [[ Colon ]] , Dash , Ellipsis , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Colon"], "t": ["Chinese punctuation marks"]}, {"label": "CONJUNCTION", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : [[ Colon ]] , << Dash >> , Ellipsis , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Colon"], "t": ["Dash"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : Colon , [[ Dash ]] , Ellipsis , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Dash"], "t": ["Chinese punctuation marks"]}, {"label": "CONJUNCTION", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : Colon , [[ Dash ]] , << Ellipsis >> , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Dash"], "t": ["Ellipsis"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : Colon , Dash , [[ Ellipsis ]] , Exclamation Mark , Question Mark , and Semicolon .", "h": ["Ellipsis"], "t": ["Chinese punctuation marks"]}, {"label": "CONJUNCTION", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : Colon , Dash , [[ Ellipsis ]] , << Exclamation Mark >> , Question Mark , and Semicolon .", "h": ["Ellipsis"], "t": ["Exclamation Mark"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : Colon , Dash , Ellipsis , [[ Exclamation Mark ]] , Question Mark , and Semicolon .", "h": ["Exclamation Mark"], "t": ["Chinese punctuation marks"]}, {"label": "CONJUNCTION", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : Colon , Dash , Ellipsis , [[ Exclamation Mark ]] , << Question Mark >> , and Semicolon .", "h": ["Exclamation Mark"], "t": ["Question Mark"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : Colon , Dash , Ellipsis , Exclamation Mark , [[ Question Mark ]] , and Semicolon .", "h": ["Question Mark"], "t": ["Chinese punctuation marks"]}, {"label": "CONJUNCTION", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts : Colon , Dash , Ellipsis , Exclamation Mark , [[ Question Mark ]] , and << Semicolon >> .", "h": ["Question Mark"], "t": ["Semicolon"]}, {"label": "HYPONYM-OF", "tokens": "Based on a corpus annotation project , this paper reports the discursive usage of 6 << Chinese punctuation marks >> in news commentary texts : Colon , Dash , Ellipsis , Exclamation Mark , Question Mark , and [[ Semicolon ]] .", "h": ["Semicolon"], "t": ["Chinese punctuation marks"]}, {"label": "FEATURE-OF", "tokens": "The [[ rhetorical patterns ]] of these << marks >> are compared against patterns around cue phrases in general .", "h": ["rhetorical patterns"], "t": ["marks"]}, {"label": "COMPARE", "tokens": "The [[ rhetorical patterns ]] of these marks are compared against << patterns around cue phrases >> in general .", "h": ["rhetorical patterns"], "t": ["patterns around cue phrases"]}, {"label": "COMPARE", "tokens": "Results show that these [[ Chinese punctuation marks ]] , though fewer in number than << cue phrases >> , are easy to identify , have strong correlation with certain relations , and can be used as distinctive indicators of nuclearity in Chinese texts .", "h": ["Chinese punctuation marks"], "t": ["cue phrases"]}, {"label": "USED-FOR", "tokens": "Results show that these [[ Chinese punctuation marks ]] , though fewer in number than cue phrases , are easy to identify , have strong correlation with certain relations , and can be used as distinctive << indicators of nuclearity >> in Chinese texts .", "h": ["Chinese punctuation marks"], "t": ["indicators of nuclearity"]}, {"label": "FEATURE-OF", "tokens": "Results show that these Chinese punctuation marks , though fewer in number than cue phrases , are easy to identify , have strong correlation with certain relations , and can be used as distinctive << indicators of nuclearity >> in [[ Chinese texts ]] .", "h": ["Chinese texts"], "t": ["indicators of nuclearity"]}, {"label": "USED-FOR", "tokens": "The << features >> based on [[ Markov random field -LRB- MRF -RRB- models ]] are usually sensitive to the rotation of image textures .", "h": ["Markov random field -LRB- MRF -RRB- models"], "t": ["features"]}, {"label": "USED-FOR", "tokens": "This paper develops an [[ anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model ]] for << modelling rotated image textures >> and retrieving rotation-invariant texture features .", "h": ["anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model"], "t": ["modelling rotated image textures"]}, {"label": "USED-FOR", "tokens": "This paper develops an [[ anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model ]] for modelling rotated image textures and << retrieving rotation-invariant texture features >> .", "h": ["anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model"], "t": ["retrieving rotation-invariant texture features"]}, {"label": "CONJUNCTION", "tokens": "This paper develops an anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model for [[ modelling rotated image textures ]] and << retrieving rotation-invariant texture features >> .", "h": ["modelling rotated image textures"], "t": ["retrieving rotation-invariant texture features"]}, {"label": "FEATURE-OF", "tokens": "To overcome the [[ singularity problem ]] of the << least squares estimate -LRB- LSE -RRB- method >> , an approximate least squares estimate -LRB- ALSE -RRB- method is proposed to estimate the parameters of the ACGMRF model .", "h": ["singularity problem"], "t": ["least squares estimate -LRB- LSE -RRB- method"]}, {"label": "USED-FOR", "tokens": "To overcome the singularity problem of the least squares estimate -LRB- LSE -RRB- method , an [[ approximate least squares estimate -LRB- ALSE -RRB- method ]] is proposed to estimate the << parameters of the ACGMRF model >> .", "h": ["approximate least squares estimate -LRB- ALSE -RRB- method"], "t": ["parameters of the ACGMRF model"]}, {"label": "USED-FOR", "tokens": "The << rotation-invariant features >> can be obtained from the [[ parameters of the ACGMRF model ]] by the one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB- .", "h": ["parameters of the ACGMRF model"], "t": ["rotation-invariant features"]}, {"label": "USED-FOR", "tokens": "The << rotation-invariant features >> can be obtained from the parameters of the ACGMRF model by the [[ one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB- ]] .", "h": ["one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB-"], "t": ["rotation-invariant features"]}, {"label": "USED-FOR", "tokens": "Significantly improved accuracy can be achieved by applying the [[ rotation-invariant features ]] to classify << SAR -LRB- synthetic aperture radar >> -RRB- sea ice and Brodatz imagery .", "h": ["rotation-invariant features"], "t": ["SAR -LRB- synthetic aperture radar"]}, {"label": "USED-FOR", "tokens": "Despite much recent progress on accurate << semantic role labeling >> , previous work has largely used [[ independent classifiers ]] , possibly combined with separate label sequence models via Viterbi decoding .", "h": ["independent classifiers"], "t": ["semantic role labeling"]}, {"label": "CONJUNCTION", "tokens": "Despite much recent progress on accurate semantic role labeling , previous work has largely used [[ independent classifiers ]] , possibly combined with separate << label sequence models >> via Viterbi decoding .", "h": ["independent classifiers"], "t": ["label sequence models"]}, {"label": "USED-FOR", "tokens": "Despite much recent progress on accurate semantic role labeling , previous work has largely used independent classifiers , possibly combined with separate << label sequence models >> via [[ Viterbi decoding ]] .", "h": ["Viterbi decoding"], "t": ["label sequence models"]}, {"label": "PART-OF", "tokens": "We show how to build a joint model of argument frames , incorporating novel [[ features ]] that model these interactions into << discriminative log-linear models >> .", "h": ["features"], "t": ["discriminative log-linear models"]}, {"label": "EVALUATE-FOR", "tokens": "This << system >> achieves an [[ error reduction ]] of 22 % on all arguments and 32 % on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank .", "h": ["error reduction"], "t": ["system"]}, {"label": "EVALUATE-FOR", "tokens": "This system achieves an [[ error reduction ]] of 22 % on all arguments and 32 % on core arguments over a state-of-the art << independent classifier >> for gold-standard parse trees on PropBank .", "h": ["error reduction"], "t": ["independent classifier"]}, {"label": "COMPARE", "tokens": "This << system >> achieves an error reduction of 22 % on all arguments and 32 % on core arguments over a state-of-the art [[ independent classifier ]] for gold-standard parse trees on PropBank .", "h": ["independent classifier"], "t": ["system"]}, {"label": "EVALUATE-FOR", "tokens": "This << system >> achieves an error reduction of 22 % on all arguments and 32 % on core arguments over a state-of-the art independent classifier for [[ gold-standard parse trees ]] on PropBank .", "h": ["gold-standard parse trees"], "t": ["system"]}, {"label": "EVALUATE-FOR", "tokens": "This system achieves an error reduction of 22 % on all arguments and 32 % on core arguments over a state-of-the art << independent classifier >> for [[ gold-standard parse trees ]] on PropBank .", "h": ["gold-standard parse trees"], "t": ["independent classifier"]}, {"label": "PART-OF", "tokens": "This system achieves an error reduction of 22 % on all arguments and 32 % on core arguments over a state-of-the art independent classifier for [[ gold-standard parse trees ]] on << PropBank >> .", "h": ["gold-standard parse trees"], "t": ["PropBank"]}, {"label": "USED-FOR", "tokens": "In order to deal with << ambiguity >> , the [[ MORphological PArser MORPA ]] is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. it combines a `` conventional '' context-free morphological grammar to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse .", "h": ["MORphological PArser MORPA"], "t": ["ambiguity"]}, {"label": "USED-FOR", "tokens": "In order to deal with ambiguity , the << MORphological PArser MORPA >> is provided with a [[ probabilistic context-free grammar -LRB- PCFG -RRB- ]] , i.e. it combines a `` conventional '' context-free morphological grammar to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse .", "h": ["probabilistic context-free grammar -LRB- PCFG -RRB-"], "t": ["MORphological PArser MORPA"]}, {"label": "USED-FOR", "tokens": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. << it >> combines a [[ `` conventional '' context-free morphological grammar ]] to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse .", "h": ["`` conventional '' context-free morphological grammar"], "t": ["it"]}, {"label": "USED-FOR", "tokens": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. it combines a [[ `` conventional '' context-free morphological grammar ]] to filter out << ungrammatical segmentations >> with a probability-based scoring function which determines the likelihood of each successful parse .", "h": ["`` conventional '' context-free morphological grammar"], "t": ["ungrammatical segmentations"]}, {"label": "USED-FOR", "tokens": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. << it >> combines a `` conventional '' context-free morphological grammar to filter out ungrammatical segmentations with a [[ probability-based scoring function ]] which determines the likelihood of each successful parse .", "h": ["probability-based scoring function"], "t": ["it"]}, {"label": "CONJUNCTION", "tokens": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. it combines a << `` conventional '' context-free morphological grammar >> to filter out ungrammatical segmentations with a [[ probability-based scoring function ]] which determines the likelihood of each successful parse .", "h": ["probability-based scoring function"], "t": ["`` conventional '' context-free morphological grammar"]}, {"label": "USED-FOR", "tokens": "In order to deal with ambiguity , the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB- , i.e. it combines a `` conventional '' context-free morphological grammar to filter out ungrammatical segmentations with a [[ probability-based scoring function ]] which determines the likelihood of each successful << parse >> .", "h": ["probability-based scoring function"], "t": ["parse"]}, {"label": "USED-FOR", "tokens": "Test performance data will show that a [[ PCFG ]] yields good results in << morphological parsing >> .", "h": ["PCFG"], "t": ["morphological parsing"]}, {"label": "HYPONYM-OF", "tokens": "[[ MORPA ]] is a fully implemented << parser >> developed for use in a text-to-speech conversion system .", "h": ["MORPA"], "t": ["parser"]}, {"label": "USED-FOR", "tokens": "[[ MORPA ]] is a fully implemented parser developed for use in a << text-to-speech conversion system >> .", "h": ["MORPA"], "t": ["text-to-speech conversion system"]}, {"label": "USED-FOR", "tokens": "MORPA is a fully implemented [[ parser ]] developed for use in a << text-to-speech conversion system >> .", "h": ["parser"], "t": ["text-to-speech conversion system"]}, {"label": "USED-FOR", "tokens": "This paper describes the framework of a << Korean phonological knowledge base system >> using the [[ unification-based grammar formalism ]] : Korean Phonology Structure Grammar -LRB- KPSG -RRB- .", "h": ["unification-based grammar formalism"], "t": ["Korean phonological knowledge base system"]}, {"label": "HYPONYM-OF", "tokens": "This paper describes the framework of a Korean phonological knowledge base system using the << unification-based grammar formalism >> : [[ Korean Phonology Structure Grammar -LRB- KPSG -RRB- ]] .", "h": ["Korean Phonology Structure Grammar -LRB- KPSG -RRB-"], "t": ["unification-based grammar formalism"]}, {"label": "USED-FOR", "tokens": "The [[ approach ]] of << KPSG >> provides an explicit development model for constructing a computational phonological system : speech recognition and synthesis system .", "h": ["approach"], "t": ["KPSG"]}, {"label": "USED-FOR", "tokens": "The approach of [[ KPSG ]] provides an explicit development model for constructing a computational << phonological system >> : speech recognition and synthesis system .", "h": ["KPSG"], "t": ["phonological system"]}, {"label": "COMPARE", "tokens": "We show that the proposed [[ approach ]] is more describable than other << approaches >> such as those employing a traditional generative phonological approach .", "h": ["approach"], "t": ["approaches"]}, {"label": "USED-FOR", "tokens": "We show that the proposed approach is more describable than other approaches such as << those >> employing a traditional [[ generative phonological approach ]] .", "h": ["generative phonological approach"], "t": ["those"]}, {"label": "USED-FOR", "tokens": "In this paper , we study the [[ design of core-selecting payment rules ]] for such << domains >> .", "h": ["design of core-selecting payment rules"], "t": ["domains"]}, {"label": "USED-FOR", "tokens": "We design two [[ core-selecting rules ]] that always satisfy << IR >> in expectation .", "h": ["core-selecting rules"], "t": ["IR"]}, {"label": "USED-FOR", "tokens": "To study the performance of our << rules >> we perform a [[ computational Bayes-Nash equilibrium analysis ]] .", "h": ["computational Bayes-Nash equilibrium analysis"], "t": ["rules"]}, {"label": "COMPARE", "tokens": "We show that , in equilibrium , our new [[ rules ]] have better incentives , higher efficiency , and a lower rate of ex-post IR violations than standard << core-selecting rules >> .", "h": ["rules"], "t": ["core-selecting rules"]}, {"label": "EVALUATE-FOR", "tokens": "We show that , in equilibrium , our new << rules >> have better incentives , higher efficiency , and a lower [[ rate of ex-post IR violations ]] than standard core-selecting rules .", "h": ["rate of ex-post IR violations"], "t": ["rules"]}, {"label": "EVALUATE-FOR", "tokens": "We show that , in equilibrium , our new rules have better incentives , higher efficiency , and a lower [[ rate of ex-post IR violations ]] than standard << core-selecting rules >> .", "h": ["rate of ex-post IR violations"], "t": ["core-selecting rules"]}, {"label": "USED-FOR", "tokens": "In this paper , we will describe a [[ search tool ]] for a huge set of << ngrams >> .", "h": ["search tool"], "t": ["ngrams"]}, {"label": "USED-FOR", "tokens": "This system can be a very useful [[ tool ]] for << linguistic knowledge discovery >> and other NLP tasks .", "h": ["tool"], "t": ["linguistic knowledge discovery"]}, {"label": "USED-FOR", "tokens": "This system can be a very useful [[ tool ]] for linguistic knowledge discovery and other << NLP tasks >> .", "h": ["tool"], "t": ["NLP tasks"]}, {"label": "CONJUNCTION", "tokens": "This system can be a very useful tool for [[ linguistic knowledge discovery ]] and other << NLP tasks >> .", "h": ["linguistic knowledge discovery"], "t": ["NLP tasks"]}, {"label": "PART-OF", "tokens": "This paper explores the role of [[ user modeling ]] in such << systems >> .", "h": ["user modeling"], "t": ["systems"]}, {"label": "USED-FOR", "tokens": "Since acquiring the knowledge for a [[ user model ]] is a fundamental problem in << user modeling >> , a section is devoted to this topic .", "h": ["user model"], "t": ["user modeling"]}, {"label": "PART-OF", "tokens": "Next , the benefits and costs of implementing a [[ user modeling component ]] for a << system >> are weighed in light of several aspects of the interaction requirements that may be imposed by the system .", "h": ["user modeling component"], "t": ["system"]}, {"label": "USED-FOR", "tokens": "[[ Information extraction techniques ]] automatically create << structured databases >> from unstructured data sources , such as the Web or newswire documents .", "h": ["Information extraction techniques"], "t": ["structured databases"]}, {"label": "USED-FOR", "tokens": "<< Information extraction techniques >> automatically create structured databases from [[ unstructured data sources ]] , such as the Web or newswire documents .", "h": ["unstructured data sources"], "t": ["Information extraction techniques"]}, {"label": "HYPONYM-OF", "tokens": "Information extraction techniques automatically create structured databases from << unstructured data sources >> , such as the [[ Web ]] or newswire documents .", "h": ["Web"], "t": ["unstructured data sources"]}, {"label": "CONJUNCTION", "tokens": "Information extraction techniques automatically create structured databases from unstructured data sources , such as the [[ Web ]] or << newswire documents >> .", "h": ["Web"], "t": ["newswire documents"]}, {"label": "HYPONYM-OF", "tokens": "Information extraction techniques automatically create structured databases from << unstructured data sources >> , such as the Web or [[ newswire documents ]] .", "h": ["newswire documents"], "t": ["unstructured data sources"]}, {"label": "EVALUATE-FOR", "tokens": "Despite the successes of these << systems >> , [[ accuracy ]] will always be imperfect .", "h": ["accuracy"], "t": ["systems"]}, {"label": "USED-FOR", "tokens": "The << information extraction system >> we evaluate is based on a [[ linear-chain conditional random field -LRB- CRF -RRB- ]] , a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary , overlapping features of the input in a Markov model .", "h": ["linear-chain conditional random field -LRB- CRF -RRB-"], "t": ["information extraction system"]}, {"label": "HYPONYM-OF", "tokens": "The information extraction system we evaluate is based on a [[ linear-chain conditional random field -LRB- CRF -RRB- ]] , a << probabilistic model >> which has performed well on information extraction tasks because of its ability to capture arbitrary , overlapping features of the input in a Markov model .", "h": ["linear-chain conditional random field -LRB- CRF -RRB-"], "t": ["probabilistic model"]}, {"label": "USED-FOR", "tokens": "The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB- , a [[ probabilistic model ]] which has performed well on << information extraction tasks >> because of its ability to capture arbitrary , overlapping features of the input in a Markov model .", "h": ["probabilistic model"], "t": ["information extraction tasks"]}, {"label": "USED-FOR", "tokens": "The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB- , a [[ probabilistic model ]] which has performed well on information extraction tasks because of its ability to capture << arbitrary , overlapping features >> of the input in a Markov model .", "h": ["probabilistic model"], "t": ["arbitrary , overlapping features"]}, {"label": "FEATURE-OF", "tokens": "The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB- , a probabilistic model which has performed well on information extraction tasks because of its ability to capture [[ arbitrary , overlapping features ]] of the << input >> in a Markov model .", "h": ["arbitrary , overlapping features"], "t": ["input"]}, {"label": "PART-OF", "tokens": "The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB- , a probabilistic model which has performed well on information extraction tasks because of its ability to capture [[ arbitrary , overlapping features ]] of the input in a << Markov model >> .", "h": ["arbitrary , overlapping features"], "t": ["Markov model"]}, {"label": "CONJUNCTION", "tokens": "We implement several techniques to estimate the confidence of both [[ extracted fields ]] and entire << multi-field records >> , obtaining an average precision of 98 % for retrieving correct fields and 87 % for multi-field records .", "h": ["extracted fields"], "t": ["multi-field records"]}, {"label": "EVALUATE-FOR", "tokens": "We implement several << techniques >> to estimate the confidence of both extracted fields and entire multi-field records , obtaining an [[ average precision ]] of 98 % for retrieving correct fields and 87 % for multi-field records .", "h": ["average precision"], "t": ["techniques"]}, {"label": "USED-FOR", "tokens": "In this paper , we use the [[ information redundancy in multilingual input ]] to correct errors in << machine translation >> and thus improve the quality of multilingual summaries .", "h": ["information redundancy in multilingual input"], "t": ["machine translation"]}, {"label": "USED-FOR", "tokens": "In this paper , we use the [[ information redundancy in multilingual input ]] to correct errors in machine translation and thus improve the quality of << multilingual summaries >> .", "h": ["information redundancy in multilingual input"], "t": ["multilingual summaries"]}, {"label": "USED-FOR", "tokens": "We demonstrate how errors in the << machine translations >> of the input [[ Arabic documents ]] can be corrected by identifying and generating from such redundancy , focusing on noun phrases .", "h": ["Arabic documents"], "t": ["machine translations"]}, {"label": "USED-FOR", "tokens": "In this paper , we propose a new [[ approach ]] to generate << oriented object proposals -LRB- OOPs -RRB- >> to reduce the detection error caused by various orientations of the object .", "h": ["approach"], "t": ["oriented object proposals -LRB- OOPs -RRB-"]}, {"label": "EVALUATE-FOR", "tokens": "In this paper , we propose a new approach to generate << oriented object proposals -LRB- OOPs -RRB- >> to reduce the [[ detection error ]] caused by various orientations of the object .", "h": ["detection error"], "t": ["oriented object proposals -LRB- OOPs -RRB-"]}, {"label": "USED-FOR", "tokens": "To this end , we propose to efficiently locate << object regions >> according to [[ pixelwise object probability ]] , rather than measuring the objectness from a set of sampled windows .", "h": ["pixelwise object probability"], "t": ["object regions"]}, {"label": "COMPARE", "tokens": "To this end , we propose to efficiently locate object regions according to [[ pixelwise object probability ]] , rather than measuring the << objectness >> from a set of sampled windows .", "h": ["pixelwise object probability"], "t": ["objectness"]}, {"label": "USED-FOR", "tokens": "We formulate the << proposal generation problem >> as a [[ generative proba-bilistic model ]] such that object proposals of different shapes -LRB- i.e. , sizes and orientations -RRB- can be produced by locating the local maximum likelihoods .", "h": ["generative proba-bilistic model"], "t": ["proposal generation problem"]}, {"label": "FEATURE-OF", "tokens": "We formulate the proposal generation problem as a generative proba-bilistic model such that << object proposals >> of different [[ shapes ]] -LRB- i.e. , sizes and orientations -RRB- can be produced by locating the local maximum likelihoods .", "h": ["shapes"], "t": ["object proposals"]}, {"label": "HYPONYM-OF", "tokens": "We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different << shapes >> -LRB- i.e. , [[ sizes ]] and orientations -RRB- can be produced by locating the local maximum likelihoods .", "h": ["sizes"], "t": ["shapes"]}, {"label": "CONJUNCTION", "tokens": "We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes -LRB- i.e. , [[ sizes ]] and << orientations >> -RRB- can be produced by locating the local maximum likelihoods .", "h": ["sizes"], "t": ["orientations"]}, {"label": "HYPONYM-OF", "tokens": "We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different << shapes >> -LRB- i.e. , sizes and [[ orientations ]] -RRB- can be produced by locating the local maximum likelihoods .", "h": ["orientations"], "t": ["shapes"]}, {"label": "USED-FOR", "tokens": "We formulate the proposal generation problem as a generative proba-bilistic model such that << object proposals >> of different shapes -LRB- i.e. , sizes and orientations -RRB- can be produced by locating the [[ local maximum likelihoods ]] .", "h": ["local maximum likelihoods"], "t": ["object proposals"]}, {"label": "USED-FOR", "tokens": "First , it helps the [[ object detector ]] handle objects of different << orientations >> .", "h": ["object detector"], "t": ["orientations"]}, {"label": "USED-FOR", "tokens": "Third , [[ it ]] avoids massive window sampling , and thereby reducing the << number of proposals >> while maintaining a high recall .", "h": ["it"], "t": ["number of proposals"]}, {"label": "EVALUATE-FOR", "tokens": "Third , << it >> avoids massive window sampling , and thereby reducing the number of proposals while maintaining a high [[ recall ]] .", "h": ["recall"], "t": ["it"]}, {"label": "EVALUATE-FOR", "tokens": "Experiments on the [[ PASCAL VOC 2007 dataset ]] show that the proposed << OOP >> outperforms the state-of-the-art fast methods .", "h": ["PASCAL VOC 2007 dataset"], "t": ["OOP"]}, {"label": "COMPARE", "tokens": "Experiments on the PASCAL VOC 2007 dataset show that the proposed [[ OOP ]] outperforms the << state-of-the-art fast methods >> .", "h": ["OOP"], "t": ["state-of-the-art fast methods"]}, {"label": "USED-FOR", "tokens": "Further experiments show that the [[ rotation invariant property ]] helps a << class-specific object detector >> achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios .", "h": ["rotation invariant property"], "t": ["class-specific object detector"]}, {"label": "COMPARE", "tokens": "Further experiments show that the rotation invariant property helps a [[ class-specific object detector ]] achieve better performance than the state-of-the-art << proposal generation methods >> in either object rotation scenarios or general scenarios .", "h": ["class-specific object detector"], "t": ["proposal generation methods"]}, {"label": "EVALUATE-FOR", "tokens": "Further experiments show that the rotation invariant property helps a << class-specific object detector >> achieve better performance than the state-of-the-art proposal generation methods in either [[ object rotation scenarios ]] or general scenarios .", "h": ["object rotation scenarios"], "t": ["class-specific object detector"]}, {"label": "EVALUATE-FOR", "tokens": "Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art << proposal generation methods >> in either [[ object rotation scenarios ]] or general scenarios .", "h": ["object rotation scenarios"], "t": ["proposal generation methods"]}, {"label": "CONJUNCTION", "tokens": "Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either [[ object rotation scenarios ]] or << general scenarios >> .", "h": ["object rotation scenarios"], "t": ["general scenarios"]}, {"label": "EVALUATE-FOR", "tokens": "Further experiments show that the rotation invariant property helps a << class-specific object detector >> achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or [[ general scenarios ]] .", "h": ["general scenarios"], "t": ["class-specific object detector"]}, {"label": "EVALUATE-FOR", "tokens": "Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art << proposal generation methods >> in either object rotation scenarios or [[ general scenarios ]] .", "h": ["general scenarios"], "t": ["proposal generation methods"]}, {"label": "PART-OF", "tokens": "This paper describes three relatively [[ domain-independent capabilities ]] recently added to the << Paramax spoken language understanding system >> : non-monotonic reasoning , implicit reference resolution , and database query paraphrase .", "h": ["domain-independent capabilities"], "t": ["Paramax spoken language understanding system"]}, {"label": "HYPONYM-OF", "tokens": "This paper describes three relatively << domain-independent capabilities >> recently added to the Paramax spoken language understanding system : [[ non-monotonic reasoning ]] , implicit reference resolution , and database query paraphrase .", "h": ["non-monotonic reasoning"], "t": ["domain-independent capabilities"]}, {"label": "HYPONYM-OF", "tokens": "This paper describes three relatively << domain-independent capabilities >> recently added to the Paramax spoken language understanding system : non-monotonic reasoning , [[ implicit reference resolution ]] , and database query paraphrase .", "h": ["implicit reference resolution"], "t": ["domain-independent capabilities"]}, {"label": "HYPONYM-OF", "tokens": "This paper describes three relatively << domain-independent capabilities >> recently added to the Paramax spoken language understanding system : non-monotonic reasoning , implicit reference resolution , and [[ database query paraphrase ]] .", "h": ["database query paraphrase"], "t": ["domain-independent capabilities"]}, {"label": "EVALUATE-FOR", "tokens": "Finally , we briefly describe an experiment which we have done in extending the << n-best speech/language integration architecture >> to improving [[ OCR accuracy ]] .", "h": ["OCR accuracy"], "t": ["n-best speech/language integration architecture"]}, {"label": "USED-FOR", "tokens": "We investigate the problem of fine-grained sketch-based image retrieval -LRB- SBIR -RRB- , where [[ free-hand human sketches ]] are used as queries to perform << instance-level retrieval of images >> .", "h": ["free-hand human sketches"], "t": ["instance-level retrieval of images"]}, {"label": "USED-FOR", "tokens": "This is an extremely challenging task because -LRB- i -RRB- visual comparisons not only need to be fine-grained but also executed cross-domain , -LRB- ii -RRB- free-hand -LRB- finger -RRB- sketches are highly abstract , making fine-grained matching harder , and most importantly -LRB- iii -RRB- [[ annotated cross-domain sketch-photo datasets ]] required for training are scarce , challenging many state-of-the-art << machine learning techniques >> .", "h": ["annotated cross-domain sketch-photo datasets"], "t": ["machine learning techniques"]}, {"label": "USED-FOR", "tokens": "We then develop a [[ deep triplet-ranking model ]] for << instance-level SBIR >> with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data .", "h": ["deep triplet-ranking model"], "t": ["instance-level SBIR"]}, {"label": "USED-FOR", "tokens": "We then develop a [[ deep triplet-ranking model ]] for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of << insufficient fine-grained training data >> .", "h": ["deep triplet-ranking model"], "t": ["insufficient fine-grained training data"]}, {"label": "USED-FOR", "tokens": "We then develop a << deep triplet-ranking model >> for instance-level SBIR with a novel [[ data augmentation ]] and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data .", "h": ["data augmentation"], "t": ["deep triplet-ranking model"]}, {"label": "CONJUNCTION", "tokens": "We then develop a deep triplet-ranking model for instance-level SBIR with a novel [[ data augmentation ]] and << staged pre-training strategy >> to alleviate the issue of insufficient fine-grained training data .", "h": ["data augmentation"], "t": ["staged pre-training strategy"]}, {"label": "USED-FOR", "tokens": "We then develop a << deep triplet-ranking model >> for instance-level SBIR with a novel data augmentation and [[ staged pre-training strategy ]] to alleviate the issue of insufficient fine-grained training data .", "h": ["staged pre-training strategy"], "t": ["deep triplet-ranking model"]}, {"label": "CONJUNCTION", "tokens": "Extensive experiments are carried out to contribute a variety of insights into the challenges of [[ data sufficiency ]] and << over-fitting avoidance >> when training deep networks for fine-grained cross-domain ranking tasks .", "h": ["data sufficiency"], "t": ["over-fitting avoidance"]}, {"label": "USED-FOR", "tokens": "Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training [[ deep networks ]] for << fine-grained cross-domain ranking tasks >> .", "h": ["deep networks"], "t": ["fine-grained cross-domain ranking tasks"]}, {"label": "USED-FOR", "tokens": "In this paper we target at generating << generic action proposals >> in [[ unconstrained videos ]] .", "h": ["unconstrained videos"], "t": ["generic action proposals"]}, {"label": "HYPONYM-OF", "tokens": "Each action proposal corresponds to a << temporal series of spatial bounding boxes >> , i.e. , a [[ spatio-temporal video tube ]] , which has a good potential to locate one human action .", "h": ["spatio-temporal video tube"], "t": ["temporal series of spatial bounding boxes"]}, {"label": "USED-FOR", "tokens": "Each action proposal corresponds to a temporal series of spatial bounding boxes , i.e. , a [[ spatio-temporal video tube ]] , which has a good potential to locate one << human action >> .", "h": ["spatio-temporal video tube"], "t": ["human action"]}, {"label": "USED-FOR", "tokens": "Assuming each action is performed by a human with meaningful motion , both [[ appearance and motion cues ]] are utilized to measure the << ac-tionness >> of the video tubes .", "h": ["appearance and motion cues"], "t": ["ac-tionness"]}, {"label": "EVALUATE-FOR", "tokens": "Assuming each action is performed by a human with meaningful motion , both appearance and motion cues are utilized to measure the [[ ac-tionness ]] of the << video tubes >> .", "h": ["ac-tionness"], "t": ["video tubes"]}, {"label": "USED-FOR", "tokens": "After picking those spatiotem-poral paths of high actionness scores , our << action proposal generation >> is formulated as a [[ maximum set coverage problem ]] , where greedy search is performed to select a set of action proposals that can maximize the overall actionness score .", "h": ["maximum set coverage problem"], "t": ["action proposal generation"]}, {"label": "USED-FOR", "tokens": "After picking those spatiotem-poral paths of high actionness scores , our action proposal generation is formulated as a maximum set coverage problem , where [[ greedy search ]] is performed to select a set of << action proposals >> that can maximize the overall actionness score .", "h": ["greedy search"], "t": ["action proposals"]}, {"label": "EVALUATE-FOR", "tokens": "After picking those spatiotem-poral paths of high actionness scores , our action proposal generation is formulated as a maximum set coverage problem , where greedy search is performed to select a set of << action proposals >> that can maximize the overall [[ actionness score ]] .", "h": ["actionness score"], "t": ["action proposals"]}, {"label": "COMPARE", "tokens": "Compared with existing [[ action proposal approaches ]] , our << action proposals >> do not rely on video segmentation and can be generated in nearly real-time .", "h": ["action proposal approaches"], "t": ["action proposals"]}, {"label": "EVALUATE-FOR", "tokens": "Experimental results on two challenging [[ datasets ]] , MSRII and UCF 101 , validate the superior performance of our << action proposals >> as well as competitive results on action detection and search .", "h": ["datasets"], "t": ["action proposals"]}, {"label": "HYPONYM-OF", "tokens": "Experimental results on two challenging << datasets >> , [[ MSRII ]] and UCF 101 , validate the superior performance of our action proposals as well as competitive results on action detection and search .", "h": ["MSRII"], "t": ["datasets"]}, {"label": "CONJUNCTION", "tokens": "Experimental results on two challenging datasets , [[ MSRII ]] and << UCF 101 >> , validate the superior performance of our action proposals as well as competitive results on action detection and search .", "h": ["MSRII"], "t": ["UCF 101"]}, {"label": "HYPONYM-OF", "tokens": "Experimental results on two challenging << datasets >> , MSRII and [[ UCF 101 ]] , validate the superior performance of our action proposals as well as competitive results on action detection and search .", "h": ["UCF 101"], "t": ["datasets"]}, {"label": "EVALUATE-FOR", "tokens": "Experimental results on two challenging datasets , MSRII and UCF 101 , validate the superior performance of our << action proposals >> as well as competitive results on [[ action detection and search ]] .", "h": ["action detection and search"], "t": ["action proposals"]}, {"label": "USED-FOR", "tokens": "This paper reports recent research into [[ methods ]] for << creating natural language text >> .", "h": ["methods"], "t": ["creating natural language text"]}, {"label": "PART-OF", "tokens": "<< KDS -LRB- Knowledge Delivery System -RRB- >> , which embodies this [[ paradigm ]] , has distinct parts devoted to creation of the propositional units , to organization of the text , to prevention of excess redundancy , to creation of combinations of units , to evaluation of these combinations as potential sentences , to selection of the best among competing combinations , and to creation of the final text .", "h": ["paradigm"], "t": ["KDS -LRB- Knowledge Delivery System -RRB-"]}, {"label": "USED-FOR", "tokens": "The Fragment-and-Compose paradigm and the [[ computational methods ]] of << KDS >> are described .", "h": ["computational methods"], "t": ["KDS"]}, {"label": "USED-FOR", "tokens": "This paper explores the issue of using different [[ co-occurrence similarities ]] between terms for separating << query terms >> that are useful for retrieval from those that are harmful .", "h": ["co-occurrence similarities"], "t": ["query terms"]}, {"label": "USED-FOR", "tokens": "This paper explores the issue of using different co-occurrence similarities between terms for separating [[ query terms ]] that are useful for << retrieval >> from those that are harmful .", "h": ["query terms"], "t": ["retrieval"]}, {"label": "COMPARE", "tokens": "This paper explores the issue of using different co-occurrence similarities between terms for separating << query terms >> that are useful for retrieval from [[ those ]] that are harmful .", "h": ["those"], "t": ["query terms"]}, {"label": "COMPARE", "tokens": "The hypothesis under examination is that [[ useful terms ]] tend to be more similar to each other than to other << query terms >> .", "h": ["useful terms"], "t": ["query terms"]}, {"label": "USED-FOR", "tokens": "Preliminary experiments with << similarities >> computed using [[ first-order and second-order co-occurrence ]] seem to confirm the hypothesis .", "h": ["first-order and second-order co-occurrence"], "t": ["similarities"]}, {"label": "CONJUNCTION", "tokens": "We propose a new [[ phrase-based translation model ]] and << decoding algorithm >> that enables us to evaluate and compare several , previously proposed phrase-based translation models .", "h": ["phrase-based translation model"], "t": ["decoding algorithm"]}, {"label": "COMPARE", "tokens": "Within our framework , we carry out a large number of experiments to understand better and explain why [[ phrase-based models ]] outperform << word-based models >> .", "h": ["phrase-based models"], "t": ["word-based models"]}, {"label": "HYPONYM-OF", "tokens": "Our empirical results , which hold for all examined language pairs , suggest that the highest levels of performance can be obtained through relatively simple << means >> : [[ heuristic learning of phrase translations ]] from word-based alignments and lexical weighting of phrase translations .", "h": ["heuristic learning of phrase translations"], "t": ["means"]}, {"label": "USED-FOR", "tokens": "Our empirical results , which hold for all examined language pairs , suggest that the highest levels of performance can be obtained through relatively simple means : << heuristic learning of phrase translations >> from [[ word-based alignments ]] and lexical weighting of phrase translations .", "h": ["word-based alignments"], "t": ["heuristic learning of phrase translations"]}, {"label": "HYPONYM-OF", "tokens": "Our empirical results , which hold for all examined language pairs , suggest that the highest levels of performance can be obtained through relatively simple << means >> : heuristic learning of phrase translations from word-based alignments and [[ lexical weighting of phrase translations ]] .", "h": ["lexical weighting of phrase translations"], "t": ["means"]}, {"label": "USED-FOR", "tokens": "Traditional [[ methods ]] for << color constancy >> can improve surface re-flectance estimates from such uncalibrated images , but their output depends significantly on the background scene .", "h": ["methods"], "t": ["color constancy"]}, {"label": "USED-FOR", "tokens": "Traditional [[ methods ]] for color constancy can improve << surface re-flectance estimates >> from such uncalibrated images , but their output depends significantly on the background scene .", "h": ["methods"], "t": ["surface re-flectance estimates"]}, {"label": "USED-FOR", "tokens": "Traditional methods for color constancy can improve << surface re-flectance estimates >> from such [[ uncalibrated images ]] , but their output depends significantly on the background scene .", "h": ["uncalibrated images"], "t": ["surface re-flectance estimates"]}, {"label": "USED-FOR", "tokens": "We introduce the multi-view color constancy problem , and present a [[ method ]] to recover << estimates of underlying surface re-flectance >> based on joint estimation of these surface properties and the illuminants present in multiple images .", "h": ["method"], "t": ["estimates of underlying surface re-flectance"]}, {"label": "USED-FOR", "tokens": "The [[ method ]] can exploit << image correspondences >> obtained by various alignment techniques , and we show examples based on matching local region features .", "h": ["method"], "t": ["image correspondences"]}, {"label": "USED-FOR", "tokens": "The method can exploit << image correspondences >> obtained by various [[ alignment techniques ]] , and we show examples based on matching local region features .", "h": ["alignment techniques"], "t": ["image correspondences"]}, {"label": "USED-FOR", "tokens": "Our results show that [[ multi-view constraints ]] can significantly improve << estimates of both scene illuminants and object color -LRB- surface reflectance -RRB- >> when compared to a baseline single-view method .", "h": ["multi-view constraints"], "t": ["estimates of both scene illuminants and object color -LRB- surface reflectance -RRB-"]}, {"label": "COMPARE", "tokens": "Our results show that << multi-view constraints >> can significantly improve estimates of both scene illuminants and object color -LRB- surface reflectance -RRB- when compared to a [[ baseline single-view method ]] .", "h": ["baseline single-view method"], "t": ["multi-view constraints"]}, {"label": "USED-FOR", "tokens": "Our contributions include a [[ concise , modular architecture ]] with reversible processes of << understanding >> and generation , an information-state model of reference , and flexible links between semantics and collaborative problem solving .", "h": ["concise , modular architecture"], "t": ["understanding"]}, {"label": "USED-FOR", "tokens": "Our contributions include a [[ concise , modular architecture ]] with reversible processes of understanding and << generation >> , an information-state model of reference , and flexible links between semantics and collaborative problem solving .", "h": ["concise , modular architecture"], "t": ["generation"]}, {"label": "CONJUNCTION", "tokens": "Our contributions include a concise , modular architecture with reversible processes of [[ understanding ]] and << generation >> , an information-state model of reference , and flexible links between semantics and collaborative problem solving .", "h": ["understanding"], "t": ["generation"]}]